Sleep breathing disorder monitoring method and system and electronic equipment

Abstract

The invention discloses a sleep breathing disorder monitoring method. The method comprises the following steps: acquiring an electroencephalogram signal, an electro-oculogram signal and a photoelectric volume pulse wave signal; performing sleep automatic staging and sleep duration statistics based on electroencephalogram and electro-oculogram signals; carrying out apnea and hypopnea event statistics on the basis of the photoelectric volume pulse wave signals; and calculating an AHI index according to the counted sleep duration ST and the number of times BSN of the apnea and hypopnea events. Furthermore, the invention also discloses a corresponding head-mounted sleep breathing disorder monitoring system and electronic equipment. According to the invention, real-time and accurate sleep staging and apnea diagnosis can be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of sleep breathing disorder monitoring, and in particular relates to a head-mounted sleep breathing disorder monitoring technology based on multimodal signals. Background Art

[0002] Sleep staging refers to the division of the stages experienced during sleep according to physiological signals at night, including rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep. In REM sleep, the body's muscle activity level reaches the lowest level of the whole night, and the eyeballs can be observed to move rapidly. It is also the period when the body is most likely to enter a dream. NREM sleep can be further divided into three stages: N1 is a light sleep stage, and people are easily awakened; N2 is a moderate sleep stage, which accounts for the largest proportion of the entire sleep time, indicating that the body has entered a deeper sleep; N3 is a deep sleep stage, also known as slow-wave sleep, which is mainly responsible for the recovery and repair of the body. The typical sleep cycle of a healthy adult generally starts from the wakeful state and enters the N1 stage of NREM sleep, gradually losing attention and experiencing slow eye movements. After a few minutes, it enters the N2 stage, at which time the body's muscle and eye movements decrease. After entering the deep sleep of the N3 stage, the muscle tension further decreases and it is not easy to be awakened. After the end of the N3 stage, it turns to the N2 stage and the N1 stage in turn, and then transitions to REM sleep. After REM sleep ends, you will enter the N1 stage again, and the cycle will repeat.

[0003] Obstructive sleep apnea (OSA) disorder is one of the common sleep disorders, characterized by repeated upper airway obstruction during sleep, which usually leads to decreased blood oxygen saturation. Under normal circumstances, it is difficult for patients to realize that they have sleep apnea, and they need to be discovered and reminded by their pillowmates, which poses a great hidden danger. At present, the simplest and most direct way to improve sleep breathing in clinical practice is to wake the patient up and let him fall asleep again. The apnea will end when the patient is awakened, and the blood oxygen saturation will gradually return to the baseline level after normal breathing is restored. An apnea event refers to a complete cessation of oral and nasal breathing airflow for more than 10 seconds during sleep; a hypopnea event refers to a decrease in the intensity (amplitude) of respiratory airflow during sleep by more than 50% compared to the baseline level, accompanied by a decrease in blood oxygen saturation of ≥4% compared to the baseline level. The apnea-hypopnea index (AHI) refers to the number of apneas and hypopneas per hour during sleep.

[0004] Polysomnography (PSG) is a multi-channel signal graph that is clinically collected through a polysomnographic monitor (also known as a PSG device) to collect bioelectric signals and physiological signals such as nasal breathing and snoring from different parts of the human body (such as the brain, eyes, and mandible), and then presented to the doctor after filtering and amplification. Polysomnography needs to be collected and recorded by professional doctors or technicians in professional institutions such as hospitals or sleep centers. It is used for the diagnosis of various sleep disorders and is the gold standard for the diagnosis of sleep apnea hypopnea syndrome. Therefore, the current clinical diagnosis and calculation of AHI usually requires the use of PSG equipment to monitor sleep breathing disorders throughout the night. However, the use of PSG equipment has obvious disadvantages and shortcomings in terms of portability and intelligence, including: (1) PSG equipment is expensive and large in size, and needs to be used in hospitals, sleep research centers and other places; (2) The use of PSG requires the assistance of professional technicians and cannot be installed and operated independently; (3) It is usually necessary to collect data from at least 12 channels, and at least 22 data cables are connected between the subject and the collector, which will make the subject feel uncomfortable and affect the accuracy of the test; (4) After collecting the sleep data for the whole night, it needs to be processed and analyzed by professionals, which consumes a lot of energy and is not real-time. In addition, PSG is the gold standard for sleep breathing disorder monitoring and is used to diagnose a variety of sleep diseases, but for sleep breathing disorders, the types of signals required are also redundant.

[0005] OSA disorder can occur at any stage of sleep. Compared with the N3 stage, the N1, N2 and REM stages are more likely to occur. When a patient has OSA in the REM stage, the apnea lasts longer and the blood oxygen saturation drops faster. Therefore, the diagnosis of sleep apnea requires not only the identification of apnea and hypopnea, but also an accurate judgment of the sleep state. According to the Manual for the Interpretation of Sleep and Related Events, sleep staging mainly relies on EEG and electrooculogram signals. Because the human body will experience rapid eye movement during rapid eye movement sleep (REM stage). Therefore, the REM stage can be accurately identified by electrooculogram signals, but due to the complex arrangement and positioning of the electrooculogram electrodes, they are located on both sides of the face and are easily compressed when sleeping on the side. Therefore, there are currently few head-mounted devices that detect electrooculogram signals. EEG signals are weak voltage signals at the μV level, and their amplitude and waveform are easily affected by the position of the collection electrode and the reference electrode. However, the existing monitoring equipment has non-standardization problems due to the different positions of the reference electrode and the collection electrode. Most of the collected signals do not meet the medical sleep staging standards in terms of amplitude and waveform, and cannot be interpreted by professional doctors. Therefore, the accuracy of the sleep staging algorithm developed based on the unknown signal quality cannot be determined. In addition, the current sleep staging algorithms are mostly based on the analysis of all-night data, which makes it difficult to achieve real-time sleep staging.

[0006] For example, the invention patent application with publication number CN116035557A and patent name “A flexible wearable system suitable for monitoring sleep apnea” discloses collecting information such as electrocardiogram, blood oxygen saturation, and body position changes on the chest and calculating AHI based on this information, but it only relies on body position changes to calculate sleep duration. Since the calculation of AHI requires sleep staging to obtain accurate sleep duration, and according to the Manual of Interpretation of Sleep and Related Events, sleep staging mainly relies on EEG and EOG signals, it is easy to cause calculation errors of AHI if the sleep duration is calculated only by body movement. At the same time, the patent uses a machine learning method to calculate AHI, which is highly dependent on manually selected features, and the stability and versatility of the algorithm are poor. For example, the invention patent application with publication number CN119344671A and patent name "A portable sleep breathing disorder monitoring device based on EEG-blood oxygen saturation-ECG" discloses the use of surface-mounted electrodes to collect frontal EEG and blood oxygen signals, and patch electrodes to collect chest ECG signals. However, the EEG electrodes are all attached to the forehead, no EOG electrodes are set, and the reference electrodes are not set according to international standards (10-20 international lead standards). Therefore, the interference of eye movement cannot be eliminated, and it is difficult to collect accurate EEG signals with obvious amplitude.

[0007] Dreem2 is a wireless sleep apnea monitoring headband developed by Dreem. It helps users understand and improve sleep quality by monitoring EEG signals, heart rate and body movement data. It is one of the most accurate consumer sleep recorders on the market and has been approved by the US FDA for sale as a Class II medical device. Dreem2 has six EEG electrodes, four of which are located on the forehead (frontal lobe) and the last two are located at the back of the brain (occipital lobe). When the device is working, one of the electrodes is used as a reference electrode and does not participate in the measurement of potential. The forehead part of the headband also has a pulse oximeter for measuring heart rate. However, the EEG electrodes of Dreem2 are set at a close distance. The four electrodes on the forehead are all located at the frontal lobe. The potential difference of the collected potential signals is small. The signals collected by multiple EEG electrodes are similar, and there is a lot of redundant information. The movement of the eyeballs will cause changes in electrical signals, which spread to the entire top of the head, with an amplitude of up to 100mV. However, the EEG signal is weak, and the general amplitude is only about 50μV. Therefore, EEG signals will be affected by eye movements, and need to be combined with EOG signals to eliminate interference. At the same time, the reference electrode of the Dreem2 headband is the electrode on the forehead or occipital region, but the reference electrode on the forehead is close to the EEG electrode, the signal similarity is high, and the potential difference is small, making it difficult to calculate effective EEG signals; and during sleep, the electrode on the occipital region is prone to move with the movement of the head, resulting in inaccurate and unstable reference potentials.

[0008] In summary, PSG devices have problems such as complex installation, non-wearability, and poor comfort; existing commercial wearable sleep apnea monitoring devices have not yet achieved simultaneous sleep state judgment and sleep apnea monitoring and accurate calculation of AHI; and the existing head sleep apnea monitoring device Dreem2 also has problems such as only using EEG signals and being easily disturbed by eye movements, and unreasonable reference electrode settings. Summary of the invention

[0009] In order to solve the above technical problems, the present invention provides a sleep breathing disorder monitoring method, which is applied to a head-mounted sleep breathing disorder monitoring system. On the premise of meeting the needs of sleep diagnosis, the selection and position of electrodes are optimized, and the problem of EEG signals being interfered by eye movements and the problem of too large common mode interference due to the close distance of electrodes is solved. In addition, multi-channel data analysis of EEG, EOG and blood oxygen signals can be performed simultaneously to achieve real-time and accurate sleep staging and apnea diagnosis. Furthermore, the present invention also discloses a corresponding head-mounted sleep breathing disorder monitoring system and electronic equipment.

[0010] The technical solution of the present invention is as follows: The first aspect of the present invention discloses a sleep apnea monitoring method, which can be applied to a head-mounted sleep apnea monitoring system, comprising: Acquire EEG signals, EOG signals and photoplethysmogram signals; Automatically divide sleep into stages and count sleep duration based on EEG and EOG signals; Apnea and hypopnea event statistics based on blood oxygen saturation calculated from photoplethysmographic signals; The AHI index was calculated based on the statistical sleep duration (ST) and the number of apnea and hypopnea events (BSN).

[0011] As an optional solution, the sleep breathing disorder monitoring method also includes: preprocessing the photoplethysmogram signal; and calculating the blood oxygen saturation based on the preprocessed photoplethysmogram signal.

[0012] As an optional solution, the sleep breathing disorder monitoring method further includes: outputting the severity level of obstructive sleep apnea (OSA) according to preset rules based on the AHI index and blood oxygen saturation.

[0013] As an optional solution, the sleep breathing disorder monitoring method further includes: when the AHI index is greater than a preset value, issuing a wake-up signal or a wake-up instruction.

[0014] As an optional solution, automatic sleep staging and sleep duration statistics, including: Preprocessing the EEG and EOG signals to form EEG and EOG signals of preset sizes; Based on the preset sleep cycle, the EEG and EOG signals of preset sizes are input into the sleep staging network to obtain the sleep staging prediction results; the sleep staging network can extract signal features and their time series relationships; the sleep staging prediction results include W stage, N1 stage, N2 stage, N3 stage and REM stage; According to the sleep stage prediction results, the sleep duration ST is calculated. The sleep duration ST = preset sleep cycle * the total number of N1 stage, N2 stage, N3 stage and REM stage in the sleep stage prediction results; Apnea and hypopnea event statistics, including: The difference between two adjacent blood oxygen saturation values ​​is calculated, and the number of times the difference value exceeds the preset threshold is counted as the number of apnea and hypopnea events (BSN).

[0015] As an optional solution, the sleep staging network includes: a convolutional function layer, which is configured to: input the EEG and EOG signals into one branch respectively, and after three convolution operations, randomly set a part of the features to 0 through the Dropout layer to obtain the output features of the two branches; transform the output features of the two branches to the same size through the Reshape layer, and splice them through the Concat layer to obtain the convolutional features; sort the convolutional features of different time periods in chronological order to form a convolutional feature sequence; the convolution operation includes convolution calculation, batch normalization, activation function and maximum pooling operation; the time series function layer, which is configured to: select the convolutional features of different times in the convolutional feature sequence, learn the relationship between the convolutional features in time, and obtain the corrected mixed features after transformation and dimensionality reduction; the sleep classification function layer, which is configured to: output the corrected mixed features through the fully connected layer and the Softmax layer to form a probability array with a value range of 0-1 for representing the prediction probability of different sleep stages, where the sleep stage corresponding to the maximum probability is the final sleep staging prediction result.

[0016] The second aspect of the present invention discloses a head-mounted sleep breathing disorder monitoring system, including a head-mounted sleep breathing disorder monitoring device and a computer program; the head-mounted sleep breathing disorder monitoring device includes a head-mounted wearable mechanism, and electrooculogram electrodes, electroencephalogram electrodes, reference electrodes, a PPG sensor and a hardware circuit arranged on the head-mounted wearable mechanism; the electrooculogram, electroencephalogram and reference electrodes each have a pair, and when the head-mounted wearable mechanism is worn, the pair of electrooculogram electrodes are respectively located below the outer canthus of the left eye and above the outer canthus of the right eye of the human body, for collecting electrooculogram signals; the pair of electroencephalogram electrodes are respectively located at the left and right sides of the frontal lobe of the human body, and the electroocul ... On the right side, a pair of reference electrodes are respectively in contact with the mastoids at the junction of the left and right external auricles and cheeks of the human body, and the EEG electrodes are used in conjunction with the reference electrodes to collect EEG signals; the PPG sensor is used to obtain the photoelectric volume accumulation pulse wave signal of the human body; the hardware circuit is used to pre-process the electrooculogram, EEG signals and photoelectric volume accumulation pulse wave signals; the computer program has one or more, which are stored in the hardware circuit and / or an electronic device that is communicatively connected to the head-mounted sleep breathing disorder monitoring device; when the computer program is run, it is used to execute the sleep breathing disorder monitoring method described in the first aspect of the present invention and any one of the optional schemes.

[0017] As an optional solution, the EOG and EEG electrodes are detachably connected to the head-mounted wearable device via a flexible Velcro tape; the Velcro tape includes a velvet surface and a hook surface, the velvet surface is fixed to the inner side of the head-mounted wearable device and has an area larger than the hook surface, and the EOG and EEG electrodes are bonded to the velvet surface area via the hook surface.

[0018] As an optional solution, when the head-mounted wearable mechanism is worn, the EEG electrodes are arranged 4-6 cm to the left and right of the inner midline of the eye mask and 0.5-2.5 cm from the upper edge; the EOG electrodes are arranged 6-8 cm to the left and right, 0.5-2.5 cm from the lower edge and 4-6 cm from the upper edge.

[0019] As an optional solution, the hardware circuit includes a microcontroller, and a signal acquisition module, a first communication module and a power supply module electrically connected to the microcontroller; the signal acquisition module is electrically connected to the electrooculogram electrodes, the electroencephalogram electrodes and the PPG sensor, and is used to pre-process the electrooculogram, electroencephalogram and human photoelectric volume pulse wave signals; the first communication module is used to realize the communication connection between the microcontroller and the electronic device.

[0020] As an optional solution, the wearable mechanism is a flexible structure; the hardware circuit is a flexible circuit board; the EOG, EEG and reference electrodes are flexible electrodes, wherein the EOG and EEG electrodes are also constructed as serpentine patterns with stretchable lines; the wires used to connect various electrodes, PPG sensors, intervention components and internal circuits are all constructed as serpentine traces with stretchable lines.

[0021] As an optional solution, the wearable mechanism is an eye mask; the eye mask has an eye mask body with a flexible cover and an elastic strap; the flexible cover includes an inner cover and an outer cover that are connected to each other; the electrooculogram electrodes, the electroencephalogram electrodes, and the PPG sensor are arranged on the inner cover; the reference electrode is arranged on the elastic strap; and the hardware circuit is arranged between the inner cover and the outer cover.

[0022] The third aspect of the present invention discloses an electronic device, including a processor, a memory and a second communication module; the electronic device is communicatively connected to a head-mounted sleep breathing disorder monitoring device through the second communication module; the processor is used to call a computer program stored in the memory to implement the sleep breathing disorder monitoring method of the first aspect of the present invention and any optional scheme.

[0023] The present invention has the following beneficial effects: (1) Compared with the traditional method, which uses the collected sleep duration of the whole night as the sleep duration to calculate the AHI index, the error caused by waking up in the middle of the night is ignored, which easily leads to a smaller AHI index. The present invention uses a head-mounted integrated device to detect and count apnea and hypopnea events while monitoring the sleep state, further improving the accuracy of AHI index calculation.

[0024] (2) The present invention uses the EEG signals and EOG signals collected by the head-mounted sleep apnea monitoring device as the basis for sleep staging. The EEG can provide characteristic waves of the brain for accurate distinction (N1, N2 and N3), and the EOG can reflect the activity of the eyeballs, which helps to identify the rapid eye movement (REM) period. The combination of the two signals can also eliminate the signal interference of EOG artifacts. Compared with using a single EEG or EOG signal, the information contained is more comprehensive, and a more accurate sleep staging can be achieved.

[0025] (3) The head-mounted sleep apnea monitoring device provided by the present invention is an integrated wearable device for the head. By optimizing the electrode type selection and electrode position design, the problem of EEG signal being interfered by eye movement and the common mode interference being too large due to the close distance of electrodes is solved. The device can simultaneously collect EEG, EOG and blood oxygen saturation signals, and simultaneously perform precise sleep staging and sleep apnea monitoring, thereby realizing real-time and accurate AHI index calculation.

[0026] In particular, the position of the reference electrode is optimized and placed on the inside of the eye mask strap. When worn, it is located at the intersection of the auricle and the cheek (near the mastoid process) to provide a reference potential signal. The potential difference between the potential signals collected by the two EEG electrodes on the left and right sides of the forehead and the reference signals of the left and right reference electrodes forms two EEG signals. At the same time, the reference electrode is located at a location with thin skin and thick bones, and is far away from the cerebral cortex. It is not easily disturbed by sweat, body movement, brain activity and other factors, and can provide a stable reference signal.

[0027] Furthermore, the positions of EEG and EOG electrodes on the head and face were optimized, and the electrode layout was optimized according to the forehead length, width, eye distance and eye width of Asian adults, so that the electrode positions can meet the international PSG wearing standards and ensure the high consistency and interpretability of signal characteristics and clinical equipment.

[0028] (4) The present invention uses flexible Velcro to achieve the coordinated use of the velvet surface and the hook surface, so as to achieve flexible adjustment and accurate positioning of the electrode position, and accommodate the deviation caused by differences in human facial structure. When wearing it for the first time, the user can accurately adjust the electrode position according to the characteristics of his or her facial structure, and no adjustment is required for subsequent wear.

[0029] (5) The present invention improves the convolutional neural network in the sleep staging algorithm, and adopts a network combination of CNN and LSTM, which can effectively extract signal features and time series relationships. In CNN, through multiple different convolutional layers, local features of EEG and EOG signals are extracted from dimensions of different sizes, which can effectively capture the rhythmic changes of EEG and eye movement signals. In LSTM, the feature sequence is used instead of the original signal, which not only reduces the computational complexity and realizes the lightweight model, but also helps LSTM to quickly and keenly perceive the changes in sleep stages over time.

[0030] (6) The present invention can calculate blood oxygen saturation in real time based on the PPG signal collected by the head-mounted sleep apnea monitoring device, and monitor the changes in oxygen saturation in real time. By identifying nocturnal hypoxia events and periodic downward trends, it can accurately capture obstructive sleep apnea (OSA) and hypopnea events. Compared with traditional respiratory airflow monitoring, the sensor of blood oxygen saturation monitoring is smaller, more comfortable to wear, and has stronger system integration.

[0031] (7) The head-mounted sleep apnea monitoring device provided by the present invention adopts flexible fabric and wireless data transmission, is highly comfortable to wear, and does not require data transmission via a data cable. At the same time, the use of flexible and stretchable electrodes and wires can meet the deformation and extension of the eye mask, build a long-term and stable skin-electrode contact interface, and achieve long-term and accurate data collection at night. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Schematic diagram of the structure of the sleep apnea monitoring eye mask Figure 1 ; Figure 2 Schematic diagram of the structure of the sleep apnea monitoring eye mask Figure 2 ; Figure 3 A front perspective view of a sleep monitoring eye mask in a wearing state; Figure 4A left perspective view of the sleep monitoring eye mask in a wearing state; Figure 5 It is a schematic diagram of the electrode structure and connecting wire structure of the sleep monitoring eye mask; Figure 6 It is a schematic diagram of bonding electrodes to Velcro; Figure 7 This is a circuit connection block diagram of a sleep apnea monitoring system; Figure 8 This is a schematic diagram of the AHI calculation process; Fig. 9 This is a schematic diagram of the network structure of the sleep staging module; Fig.10 The structure diagram of the convolutional neural network for extracting features; Fig.11 Schematic diagram of the blood oxygen saturation calculation process.

[0033] Figure markings: 1-eye mask body, 2-inner cover, 3-outer cover, 4-ear-hook elastic strap, 5-EEG left electrode, 6-EEG right electrode, 7-EOG left electrode, 8-EOG right electrode, 9-reference left electrode, 10-reference right electrode, 11-PPG sensor, 12-hardware circuit, 13-power supply battery, 14-switch, 15-LED light, 161-Velcro hook surface, 162-Velcro suede surface. DETAILED DESCRIPTION

[0034] The technical solution of the present invention will be clearly and completely described below in conjunction with specific embodiments and drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] In the description of the present invention, if there are terms indicating orientation or positional relationship such as "upper", "lower", "front", "back", "inside", "outside", "left", "right", etc., they are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, if there are terms such as "first" and "second", they are used to distinguish similar objects, and are not necessarily used to describe a specific order or relative importance. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood in combination with specific circumstances. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, but may include other units that are not clearly listed or inherent to these products or devices.

[0036] like Figure 1 As shown, Embodiment 1 of the present invention discloses a sleep breathing disorder monitoring eye mask, which mainly includes an eye mask body 1, a pair of EEG electrodes (electrode 5 and electrode 6), a pair of EOG electrodes (electrode 7 and electrode 8), a pair of reference electrodes (electrode 9 and electrode 10), a PPG sensor 11, a hardware circuit 12 and a power supply battery 13. The eye mask body 1 includes a mask surface and an ear-hook elastic band 4, and the mask surface is divided into an inner layer mask surface 2 and an outer layer mask surface 3. Among them, the inner layer mask surface 2 and the outer layer mask surface 3 can be made of cotton cloth materials with good flexibility, comfort and breathability, and can also be made of materials such as silk, leather and elastic fiber. The inner layer mask surface 2 and the outer layer mask surface 3 can be fixedly connected by edges to form a whole. The ear-hook elastic band 4 includes two, which are fixedly connected to the two sides of the eye mask body 1 respectively, and are used to fix the eye mask body 1 on the head. Materials such as spandex, polyester and elastic fiber can be used. In other embodiments, the elastic band can also be a headband type, and the present invention does not limit it. It can be understood that the eye mask body composed of the inner cover 2, the outer cover 3 and the ear-hanging elastic strap 4 is a head-mounted wearable mechanism for carrying the above-mentioned electrodes and devices.

[0037] On the inner side of the eye mask body 1 (i.e. the side in contact with the skin), a pair of EEG electrodes, namely electrodes 5 and 6, are symmetrically fixed on the left and right sides above the inner layer cover 2, and a pair of EOG electrodes, namely electrodes 7 and 8, are fixed on the lower left and upper right sides of the inner layer cover 2. Figure 3 As shown, when the eye mask is worn, the EEG electrodes are located exactly on the left and right sides of the human body's frontal lobe (Fp1 and Fp2 in the standard EEG position) to collect EEG potential signals; the EOG electrodes are located about 1 cm below the outer canthus of the left eye and about 1 cm above the outer canthus of the right eye, and the EOG signal is the potential difference between the two EOG electrodes. A pair of reference electrodes, namely the left reference electrode 9 and the right reference electrode 10, are fixed on the inner sides of the left and right ear-hanging elastic straps 4, respectively. Figure 4 As shown, when the eye mask is worn, a pair of reference electrodes are in contact with the mastoids at the junction of the left and right auricles and cheeks, respectively, to provide a stable, low-amplitude reference potential. The potential difference between the left and right EEG electrodes and the left and right reference electrodes forms two EEG signals respectively. The above electrodes can be fixed to the inner cover 2 or the ear-hook elastic strap 4 by gluing with ointment-like glue such as silicone.

[0038] It is worth noting that the potential signal collected by the two EEG electrodes on the left and right sides of the forehead in the present invention forms two EEG signals with the potential difference of the reference signal of the left and right reference electrodes. Compared with the occipital region, the area near the mastoid process is less affected by the movement of the head, and there is no complex external signal interference (such as electrocardiogram signal). Compared with the forehead and the tip of the nose, the skin near the mastoid process is thinner and the amount of sweating is lower, which ensures the long-term stability of the electrode-skin interface. At the same time, the mastoid process behind the ear is a raised bone, and the thicker bone structure is conducive to isolating the activity signal of the brain, and the mastoid process is located in the middle and rear of the entire skull, and is far away from the main electrical activity area of ​​the cerebral cortex, which can provide a reference potential that is not affected by brain activity as a reference signal.

[0039] Furthermore, the present invention designs specific electrode placement positions according to the anatomical characteristics of the Asian human body. Since the length of the human forehead (hairline to the upper edge of the eye) is about 6-8 cm, the width of the forehead (from the hairline on both sides to the inner side of the eye) is about 10-12 cm, the distance between the eyes (the horizontal distance between the inner corners of the two eyes) is about 2.5-3.5 cm, and the length of the eye (the horizontal distance from the corner of the eye to the corner of the eye) is about 2.2-3.0 cm. Here, the EEG electrodes (electrode 5 and electrode 6) can be arranged 4-6 cm to the left and right of the midline and 0.5-2.5 cm away from the upper edge, the LEOG electrode (electrode 7) can be arranged 6-8 cm to the left and right and 0.5-2.5 cm away from the lower edge, and the REOG electrode (electrode 8) can be arranged 4-6 cm away from the upper edge.

[0040] Furthermore, the present invention can also be used in conjunction with flexible Velcro to ensure the position. The flexible Velcro can realize the movable position of the monitoring electrodes (i.e., EEG electrodes and EOG electrodes), ensuring that the position can be reused after being set once, and the position is always within the ideal range. Velcro generally includes a velvet surface and a hook surface, and the velvet surface and the hook surface can be used together to achieve the adhesion of the two. Figure 6 As shown, for example, the monitoring electrode size is 1×1 cm 2 The monitoring electrode is attached to the Velcro hook surface 161 of the same shape and size through the Velcro adhesive, and the Velcro velvet surface 162 is 2×2 cm in size. 2 The Velcro velvet surface 162 is attached to the inner cover surface 2 through the Velcro adhesive. In this way, when wearing it for the first time, the user can adjust the specific position of the monitoring electrode on the Velcro velvet surface 162 by himself according to the instructions based on the characteristics of his facial structure, and no adjustment is required for subsequent wearing.

[0041] In order to ensure good contact between the electrodes and the skin, various electrodes (EEG electrodes, EOG electrodes and reference electrodes) need to be flexible and able to deform and adapt to different wearing conditions like the fabric of the eye mask. In addition, the elastic ear-hook straps of the eye mask need to be stretched repeatedly when worn. Therefore, the reference electrode and connecting wires must also have sufficient stretchability to avoid breakage during the stretching process, thereby ensuring stable signal collection and transmission. Figure 5 As shown in (a) in the figure, these electrodes can be made of ultra-thin metal foil with a serpentine pattern and high-precision laser cutting. Compared with hard electrodes such as silver / silver chloride and gold cup electrodes, flexible electrodes have higher skin conformality and comfort, and can effectively avoid the problem of poor contact due to uneven skin. Figure 5 As shown in (b), the wires between the electrodes and the hardware circuit can also be serpentine, which can effectively avoid circuit breakage caused by stretching and deformation of the eye mask. Furthermore, ultra-thin and stretchable polyimide film can be used to insulate and encapsulate the serpentine line through hot melting and hot pressing to ensure stable and reliable signal transmission.

[0042] The PPG sensor 11 is mainly composed of a light source (eg, LED) and a photodetector. The PPG sensor 11 is fixed to the upper middle portion of the inner cover surface by digging a hole in the surface of the inner cover surface 2. Figure 3 As shown, when the eye mask is worn, the PPG sensor 11 is located at the center of the forehead, and the photoplethysmographic (PPG) signal of the forehead is monitored by the reflected PPG (photoplethysmography) signal (hereinafter referred to as "PPG signal"). PPG is a non-invasive technology that measures changes in blood flow in blood vessels by detecting optical changes in the skin. When the light emitted by the light source is absorbed by the skin, blood vessels and blood, part of the light is reflected and received by the detector. As hemoglobin combines with oxygen, it absorbs light of different wavelengths, thereby affecting the light intensity received by the sensor. In this embodiment, the PPG sensor 11 can specifically use the MAX30102 sensor.

[0043] Combination Figure 2 and Figure 3 As shown, the hardware circuit 12 and the power supply battery 13 can be fixed between the inner cover 2 and the outer cover 3 by gluing, and the eye mask is just located at the center of the forehead to balance the overall weight when worn. The multiple pin interfaces of the hardware circuit are respectively connected to the EEG electrodes, the EO electrodes, the reference electrodes and the PPG sensor through wires to synchronously collect EEG, EO and photoelectric volume pulse wave signals. A switch 14 electrically connected to the hardware circuit 12 is also provided on the top of the outer cover 3, and the switch state of the sleep breathing disorder monitoring eye mask is controlled by the switch 14.

[0044] like Figure 7As shown, the hardware circuit 12 is integrated with a microcontroller, which is mainly used for data calculation and module function control, and an STM32 controller can be selected. The hardware circuit 12 is also integrated with a signal acquisition module having multiple signal acquisition units. The signal acquisition unit integrates an analog amplifier circuit and an analog-to-digital conversion circuit, which are respectively used for amplification and analog-to-digital conversion of EEG, electrooculogram and photoelectric volume pulse wave signals. For example, the analog amplifier circuit in the EEG processing unit first amplifies the EEG signal with a small amplitude; the analog-to-digital conversion circuit then converts the amplified analog signal into a digital signal. The hardware circuit 12 is also integrated with a Bluetooth communication module and a power management module, and the microcontroller sends the analog-to-digital converted signal to the mobile terminal through the Bluetooth communication module. The power management module is connected to the power supply battery 13, which is mainly used to power the microcontroller and each functional module in the hardware circuit 12. Further, the hardware circuit 12 is also configured with an LED indication module for indicating the on and off status of the system. The hardware circuit 12 can use a flexible circuit board (FPC) made of materials such as polyimide or polyester film as a substrate, which is highly reliable and also has the characteristics of light weight, thin thickness and good bendability.

[0045] Embodiment 2 of the present invention provides a mobile terminal, which together with the sleep breathing disorder monitoring eye mask in Embodiment 1 constitutes a sleep breathing disorder monitoring system. The mobile terminal includes a processor and a memory electrically connected to the processor, a communication module, and an alarm warning module. The mobile terminal can specifically be an electronic device such as a smart phone, a tablet computer, a smart watch, a smart bracelet, a dedicated supporting terminal, etc. The alarm warning module can specifically be implemented through a built-in speaker of the mobile terminal.

[0046] like Figure 8 As shown, the functions of the mobile terminal mainly include automatic sleep staging and apnea and hypopnea event statistics, and the AHI value is calculated and updated in real time according to the sleep staging, apnea and hypopnea event statistics results. Correspondingly, the processor of the mobile terminal is configured with a sleep analysis module, an apnea and hypopnea event statistics module, and an AHI calculation module. Among them, the sleep analysis module mainly includes a sleep preprocessing unit, a sleep staging unit, and a sleep duration statistics unit, which are respectively used to preprocess the EEG and EOG signals, obtain the prediction results of the sleep stage, and perform sleep duration statistics. The apnea and hypopnea event statistics module mainly includes a blood oxygen preprocessing unit, a blood oxygen saturation calculation unit, and an event statistics unit, which are respectively used to preprocess the PPG signal, calculate the blood oxygen saturation, and apnea and hypopnea event statistics. The AHI calculation module calculates the AHI value in real time based on the sleep staging results of the sleep analysis module and the event statistics results of the apnea and hypopnea event statistics module.

[0047] In a specific embodiment, the acquisition frequency of EEG and EOG in the hardware circuit is set to 100Hz, and 30s is used as the cycle of sleep staging. The EEG and EOG signals are low-pass filtered in the range of 0-30Hz by the sleep preprocessing unit in the sleep analysis unit. In addition, in order to avoid the marginal effect caused by filtering, the length of the input signal in the filtering needs to be greater than the length of one cycle (30s), and it is re-cut to the length of one cycle after filtering. In this example, the time lag method is adopted, that is, the filtering start time is 10s after the data collection is completed, and the input signal with a length of 50s is filtered. The filtered signal is vertically spliced ​​to form an EEG signal (array 1) of (2, 3000) size and an EOG signal (array 2) of (1, 3000) size as the result of sleep preprocessing, and input into the sleep staging module.

[0048] like Fig. 9 As shown in FIG, the sleep staging unit mainly includes three functional layers connected in sequence, namely, the convolutional functional layer, the time series functional layer and the sleep classification functional layer. Fig.10As shown in the figure, array 1 and array 2 pass through three consecutive convolution layers respectively. In each convolution layer, the array obtains the high-dimensional features of the array through one-dimensional convolution calculation (Conv1D), and then uses batch normalization (BN) to transform the feature values ​​to a standard normal distribution with a mean of 0 and a variance of 1; the standard feature array is nonlinearly mapped through the ReLU activation function, and then the local maximum value of the array is selected by the maximum pooling layer (MaxPool) to reduce the size of the array and obtain a single-layer convolution feature array. Then, the single-layer convolution feature array continues to input the next convolution layer, repeating the convolution calculation, batch normalization, activation function and maximum pooling operations. After three convolution layers, the obtained three-layer convolution feature array passes through the Dropout layer, randomly setting some features to 0 to reduce the risk of network overfitting, and obtaining the output features of the three-layer convolution (i.e., the output features of one branch). Since the convolution layer parameters of the two branches of array 1 and array 2 are different, the extracted feature sizes are also different. Therefore, it is necessary to transform the output features of the two branches to the same size through the Reshape layer and concatenate them through the Concat layer to obtain the final convolution feature (CNN feature). The CNN features of different time periods are sorted in chronological order to form a convolution feature sequence. In the time series function layer (LSTM), the CNN features of different times in the convolution feature sequence are selected to learn the temporal relationship of the CNN features, transform and reduce the CNN features, and obtain the corrected mixed features. Finally, in the sleep classification function layer, the corrected mixed features are mapped from high-dimensional features to an output array of size (1,5) through the fully connected layer (FC). The Softmax layer converts the output array into a probability array according to the size of the output array. The value of each element in the probability array is between 0 and 1, and the sum of all elements is 1. The values ​​in the probability array represent the predicted probabilities of five different sleep stages, including W (wakefulness), N1, N2, N3, and REM. The sleep stage corresponding to the maximum probability is the final prediction result.

[0049] The sleep duration statistics unit can calculate the wearer's sleep duration (ST) based on the sleep stage prediction results, that is, the cycle length (30s) × the total number of sleep stage prediction results (N1+N2+N3+REM).

[0050] Combination Fig.11As shown, the red light (Red) and infrared light (IR) emitted by the light source in the PPG sensor 11 are reflected by the skin and received by the detector to form a photoelectric volumetric pulse wave signal. The blood oxygen saturation is calculated by taking the beat-by-beat blood oxygen calculation method as an example. First, the red light and infrared light in the photoelectric volumetric pulse wave signal are respectively low-pass filtered by the blood oxygen preprocessing unit at 5Hz. After filtering, the peaks and troughs of the two signal waveforms are respectively found, and the absolute value of the difference between the peak and the trough of a certain beat is taken as the AC value AC of this beat, and the average value of all low-pass filtered signals between one peak and the next peak is taken as the DC value DC of this beat.

[0051] Then, the blood oxygen saturation calculation unit can obtain the blood oxygen saturation of this beat using the following formula (1).

[0052] (1) In the formula, Indicates blood oxygen saturation, represents the red light AC component, represents the DC component of red light, Indicates the infrared light AC component, Represents the DC component of infrared light.

[0053] In the above method, the calculation frequency of the blood oxygen saturation value is related to the heart beat frequency (heart rate). In order to stabilize the sampling rate of the blood oxygen saturation signal, the time window method can also be used to calculate the blood oxygen saturation value every second to ensure that the sampling rate of the blood oxygen saturation is 1Hz. First, set a time window of a specific length (Take 5 seconds as an example), the time window stores the newly collected photoelectric volume pulse wave every 1 second, and releases the photoelectric volume pulse wave of the first 1 second. In the data of the time window, find the main peak and trough value of the photoelectric volume pulse wave, take the average value of the absolute value of the difference between the peak and trough of each beat as the AC value AC in this time window, take the average value of all numbers as the DC value DC of the beat in this time window, and then calculate the blood oxygen saturation according to formula (2).

[0054] According to the Manual for the Judgment of Sleep and Related Events published by the American Academy of Sleep Medicine, when the blood oxygen saturation drops by ≥4% compared to the baseline value, it can be used to judge a hypopnea event. When the wearer has apnea, the respiratory airflow almost stops and the blood oxygen saturation will drop faster. The difference in blood oxygen saturation can be calculated according to formula (2): It is understandable that for the time window method, The event statistics unit counts the number of times the difference exceeds the specified threshold, that is, the number of apnea and hypopnea events BSN. In this embodiment, the threshold is selected as 4%, but it can also be adjusted according to the severity of sleep apnea and individual differences.

[0055] (2) The AHI calculation module calculates the AHI index based on the sleep duration ST, the number of apnea and hypopnea events BSN according to the following formula (3).

[0056] (3) Furthermore, the present invention can also set a wake-up threshold, and when AHI> the wake-up threshold, the mobile terminal wakes up the wearer by means of an alarm. In this embodiment, the wake-up threshold is set to 15, that is, the wearer with moderate sleep apnea is awakened. In other embodiments, bone conduction headphones, ringing and other modules can also be added to the ear-hook elastic strap 4 of the sleep apnea monitoring eye mask, and after receiving the relevant instructions sent by the mobile terminal, the corresponding operation is performed to wake up the wearer.

[0057] It is worth noting that in the present invention, since ST and BSN can be analyzed in real time every 30s of sleep cycle signal collected, AHI does not need to be calculated after monitoring for the whole night, and can be calculated in real time. In fact, AHI also changes in real time with the time of falling asleep, and the wearer's AHI changes can also be understood through the real-time calculation of AHI. According to the dynamic and instantly responsive AHI, the severity of the wearer's apnea throughout the night can be accurately analyzed, and the awakening threshold can be set according to actual needs, so that sleep intervention can be carried out in time in severe apnea to avoid accidents.

[0058] Furthermore, the present invention can also judge the severity of OSA of obstructive sleep apnea according to the AHI index and blood oxygen saturation. As shown in Table 1, according to the "Guidelines for Primary Diagnosis and Treatment of Obstructive Sleep Apnea in Adults", the severity of OSA of obstructive sleep apnea in adults can be divided into three categories: mild, moderate and severe, based on AHI and blood oxygen saturation. Therefore, the wearer's OSA severity level can be output according to this standard.

[0059] Table 1. Classification of obstructive sleep apnea (OSA) in adults

[0060] It is understandable that in other embodiments, the functions implemented by the mobile terminal can also be integrated into the hardware circuit of the sleep breathing disorder monitoring eye mask, and the relevant data calculation and analysis can be performed through the microcontroller.

[0061] In summary, the present invention designs an integrated wearable head device based on EEG, EOG and blood oxygen multimodal signals, and adopts flexible electronic fabrics to ensure high wearing comfort; on the premise of meeting the needs of sleep diagnosis, the selection of electrodes is optimized to solve the problems of EEG signals being interfered by eye movements and excessive common-mode interference due to the close distance of electrodes; the reference electrode is set near the mastoid of the auricle to ensure the stability of the reference potential; it can simultaneously perform wireless transmission and multi-channel data analysis of EEG, EOG and blood oxygen signals to perform sleep staging and sleep apnea diagnosis.

[0062] When the sleep breathing disorder monitoring system disclosed in the embodiment of the present invention is used, the power switch 13 is first turned on to start the sleep breathing disorder monitoring eye mask, and the LED light 14 flashes to indicate that the system is turned on, and the mobile terminal establishes wireless communication between devices. When the user wears the smart eye mask, the position of the eye mask can be slightly adjusted by hand to ensure that the EEG electrodes (5, 6), the EOG electrodes (7, 8), the reference electrodes (9, 10) and the PPG sensor 11 are located in the correct position and in good contact. After the mobile terminal establishes a connection with the sleep breathing disorder monitoring eye mask, the LED light remains on and sleep breathing disorder monitoring begins. The hardware circuit in the sleep breathing disorder monitoring eye mask collects EEG, EOG and blood oxygen saturation signals in real time, amplifies and converts them into analog-to-digital circuits respectively, and then transmits them wirelessly to the mobile terminal. The mobile terminal will analyze the EEG and EOG data in real time based on the received various signals (EEG signals, EOG signals, etc.) to automatically stage sleep and calculate the blood oxygen saturation signal to perform statistics on sleep apnea and hypopnea events, and calculate AHI in real time. If apnea triggers an early warning, the wearer is awakened by the alarm of the sleep breathing disorder monitoring eye mask. After the sleep is over, the power switch 13 is turned off, and the mobile terminal stops receiving data and no longer displays the analysis results of sleep stages and apnea.

[0063] Finally, it should be noted that although the embodiments of the present invention are described above in conjunction with the accompanying drawings, the present invention is not limited to the above-mentioned specific embodiments and application fields, and the above-mentioned specific embodiments are only illustrative and instructive, rather than restrictive. Under the guidance of this specification, a person of ordinary skill in the art can also make many forms without departing from the scope of protection of the claims of the present invention, all of which belong to the scope of protection of the present invention.

Claims

1. A sleep apnea monitoring method, characterized in that: Applied to head-mounted sleep apnea monitoring system, including: Acquire EEG signals, EOG signals and photoplethysmogram signals; Automatically divide sleep into stages and count sleep duration based on the EEG and EOG signals; Apnea and hypopnea event statistics based on blood oxygen saturation calculated from photoplethysmographic signals; The AHI index was calculated based on the statistical sleep duration (ST) and the number of apnea and hypopnea events (BSN).

2. The sleep apnea monitoring method according to claim 1, wherein: Also includes: Preprocessing the photoplethysmography signal; Blood oxygen saturation is calculated based on the preprocessed photoplethysmography signal.

3. The sleep apnea monitoring method according to claim 1, wherein: Also includes: Based on the AHI index and the blood oxygen saturation, the severity of the obstructive sleep apnea (OSA) is output according to a preset rule.

4. The sleep apnea monitoring method according to claim 1, wherein: Also includes: When the AHI index is greater than the preset value, a wake-up signal or wake-up command is issued.

5. The sleep apnea monitoring method according to any one of claims 1 to 4, characterized in that: The automatic sleep staging and sleep duration statistics include: Preprocessing the EEG and EOG signals to form EEG and EOG signals of preset sizes; Based on the preset sleep cycle, the EEG and EOG signals of preset sizes are respectively input into the sleep staging network to obtain the sleep staging prediction results; the sleep staging network can extract signal features and their time series relationship; the sleep staging prediction results include W stage, N1 stage, N2 stage, N3 stage and REM stage; According to the sleep stage prediction result, the sleep duration ST is calculated, wherein the sleep duration ST=preset sleep cycle*the total number of N1 stage, N2 stage, N3 stage and REM stage in the sleep stage prediction result; The apnea and hypopnea event statistics include: The difference between two adjacent blood oxygen saturation values ​​is calculated, and the number of times the difference value exceeds the preset threshold is counted as the number of apnea and hypopnea events (BSN).

6. The sleep apnea monitoring method according to claim 5, characterized in that: The sleep staging network includes: The convolution function layer is configured as follows: inputting the EEG and EOG signals into one branch respectively, and after three convolution operations, randomly setting a part of the features to 0 through the Dropout layer to obtain the output features of the two branches; transforming the output features of the two branches into the same size through the Reshape layer, and splicing them through the Concat layer to obtain the convolution features; sorting the convolution features of different time periods in chronological order to form a convolution feature sequence; the convolution operation includes convolution calculation, batch normalization, activation function and maximum pooling operation; The time series function layer is configured to: select convolution features at different times in the convolution feature sequence, learn the temporal relationship of the convolution features, and obtain the modified mixed features after transformation and dimensionality reduction; The sleep classification function layer is configured to: pass the modified mixed features through the fully connected layer and the Softmax layer to output a probability array with a value range of 0-1 for representing the prediction probability of different sleep stages, where the sleep stage corresponding to the maximum probability is the final sleep stage prediction result.

7. A head-mounted sleep apnea monitoring system, characterized in that: Includes a head-mounted sleep apnea monitoring device and a computer program; The head-mounted sleep breathing disorder monitoring device comprises a head-mounted wearable mechanism, and electrooculogram electrodes, electroencephalogram electrodes, reference electrodes, a PPG sensor and a hardware circuit arranged on the head-mounted wearable mechanism; The electrooculogram, electroencephalogram and reference electrodes each have a pair, and when the head-mounted wearable mechanism is worn, the pair of electrooculogram electrodes are respectively located below the outer canthus of the left eye and above the outer canthus of the right eye of the human body, for collecting electrooculogram signals; A pair of EEG electrodes are respectively located on the left and right sides of the human frontal lobe, and a pair of reference electrodes are respectively in contact with the mastoids at the junction of the left and right external auricles and cheeks of the human body. The EEG electrodes and the reference electrodes are used in conjunction with each other to collect EEG signals. The PPG sensor is used to obtain a human photoplethysmogram signal; The hardware circuit is used for preprocessing electrooculogram, electroencephalogram and photoplethysmogram signals; The computer program has one or more programs stored in a hardware circuit and / or an electronic device that is communicatively connected to a head-mounted sleep breathing disorder monitoring device; When the computer program is executed, it is used to execute the sleep breathing disorder monitoring method according to any one of claims 1 to 6.

8. The head-mounted sleep apnea monitoring system according to claim 7, characterized in that: The electrooculogram and electroencephalogram electrodes are detachably connected to the head-mounted wearable device via a flexible Velcro tape; the Velcro tape includes a velvet surface and a hook surface, the velvet surface is fixed to the inner side of the head-mounted wearable device and has an area larger than the hook surface, and the electrooculogram and electroencephalogram electrodes are bonded to the velvet surface area via the hook surface.

9. The head-mounted sleep apnea monitoring system according to claim 7, characterized in that: When the head-mounted wearable mechanism is worn, the EEG electrodes are arranged 4-6 cm to the left and right of the inner midline of the eye mask and 0.5-2.5 cm from the upper edge; the electrooculogram electrodes are arranged 6-8 cm to the left and right and 0.5-2.5 cm from the lower edge and 4-6 cm from the upper edge.

10. The head-mounted sleep apnea monitoring system according to any one of claims 7 to 9, characterized in that: The hardware circuit includes a microcontroller, and a signal acquisition module, a first communication module and a power supply module electrically connected to the microcontroller; The signal acquisition module is electrically connected to the electrooculogram electrodes, the electroencephalogram electrodes and the PPG sensor, and is used to pre-process the electrooculogram, electroencephalogram and human photoelectric volume pulse wave signals; The first communication module is used to realize the communication connection between the microcontroller and the electronic device.

11. The head-mounted sleep apnea monitoring system according to any one of claims 7 to 9, characterized in that: The wearable mechanism is a flexible structure; the hardware circuit is a flexible circuit board; the EOG, EEG and reference electrodes are flexible electrodes, wherein the EOG and EEG electrodes are also constructed as serpentine patterns with stretchable lines; the wires used to connect various electrodes, PPG sensors, intervention components and internal circuits are all constructed as serpentine traces with stretchable lines.

12. The head-mounted sleep apnea monitoring system according to claim 11, characterized in that: The wearable mechanism is an eye mask; the eye mask has an eye mask body with a flexible cover and an elastic strap; the flexible cover includes an inner cover and an outer cover that are connected to each other; the electrooculogram electrodes, electroencephalogram electrodes, and PPG sensors are arranged on the inner cover; the reference electrode is arranged on the elastic strap; and the hardware circuit is arranged between the inner cover and the outer cover.

13. An electronic device, characterized in that: It comprises a processor, a memory and a second communication module; the electronic device is communicatively connected to a head-mounted sleep breathing disorder monitoring device via the second communication module; the processor is used to call a computer program stored in the memory to implement the sleep breathing disorder monitoring method described in any one of claims 1 to 6.

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