391 results about "Baseline drift" patented technology

An adaptive alarm system is responsive to a physiological parameter so as to generate an alarm threshold that adapts to baseline drift in the parameter and reduce false alarms without a corresponding increase in missed true alarms. The adaptive alarm system has a parameter derived from a physiological measurement system using a sensor in communication with a living being. A baseline processor calculates a parameter baseline from a parameter trend. Parameter limits specify an allowable range of the parameter. An adaptive threshold processor calculates an adaptive threshold from the parameter baseline and the parameter limits. An alarm generator is responsive to the parameter and the adaptive threshold so as to trigger an alarm indicative of the parameter crossing the adaptive threshold. The adaptive threshold is responsive to the parameter baseline so as to increase in value as the parameter baseline drifts to a higher parameter value and to decrease in value as the parameter baseline drifts to a lower parameter value.

An adaptive alarm system is responsive to a physiological parameter so as to generate an alarm threshold that adapts to baseline drift in the parameter and reduce false alarms without a corresponding increase in missed true alarms. The adaptive alarm system has a parameter derived from a physiological measurement system using a sensor in communication with a living being. A baseline processor calculates a parameter baseline from a parameter trend. Parameter limits specify an allowable range of the parameter. An adaptive threshold processor calculates an adaptive threshold from the parameter baseline and the parameter limits. An alarm generator is responsive to the parameter and the adaptive threshold so as to trigger an alarm indicative of the parameter crossing the adaptive threshold. The adaptive threshold is responsive to the parameter baseline so as to increase in value as the parameter baseline drifts to a higher parameter value and to decrease in value as the parameter baseline drifts to a lower parameter value.

The invention provides an intelligent human body sign mobile phone monitoring system, which comprises a human body sign acquisition module, a monitoring mobile phone and a cloud server, wherein the human body sign acquisition module is used for monitoring the body sign signal of a monitored object; the monitoring mobile phone is used for receiving body sign signals of the human body sign acquisition module, preprocessing the body sign signals so as to eliminate baseline drift to obtain preprocessed body sign data, storing the preprocessed body sign data and analyzing the body sign data; and the cloud server is used for receiving an equipment recognition code of the monitoring mobile phone as well as preprocessed sign signals and alarm signals, storing the body sign data and the alarm signals in corresponding items, in a database, of the monitored object, analyzing the body sign data and the alarm signals so as to generate medical advice information and transmitting the medical advice information to the corresponding monitoring mobile phone and preset associated terminal equipment. The monitoring system disclosed by the invention has the characteristics of mobile operation, simple and convenient use, long-term continuous work supporting, diagnosis result intelligent display, abnormal physiological status alarm, wireless data transmission and the like.

The invention provides an electrocardiosignal processing method and an electrocardiosignal processing system which are applicable to the technical field of data processing. The electrocardiosignal method includes: collecting electrocardiosignals; preprocessing collected electrocardiosignals; disintegrating preprocessed electrocardiosignals into single-cycled electrocardiosignal groups and subjecting each single-cycled electrocardiosignal in the single-cycled electrocardiosignal groups to normalization processing; performing the single-cycled electrocardiosignal subjected to the normalization processing to polynomial fitting to acquire fitting parameters; allowing pre-established disaggregated models to perform classification and identification on the electrocardiosignals according to the fitting parameters to acquire identification results. The polynomial fitting parameters adopted as feature vectors of electrocardiosignal classification are better in robustness, and errors brought by baseline drift of the electrocardiosignals and heart rate variation can be effectively avoided through the normalization processing performed on the single-circled electrocardiosignals.

The invention provides a method for detecting an R wave crest of an electrocardiosignal. The method takes point to point difference vector as a base characteristic. The base characteristic has translational and rotational invariance and can overcome influences of baseline drift of the electrocardiosignal. Meanwhile, logarithm polar coordinate conversion is carried out on the difference vector to measure similarity of waveforms. The measurement is sensitive to morphological characteristics of the adjacent waveforms, can capture the whole contour information of the waveforms at the same time, and has robustness on waveform swing. In addition, the method can effectively eliminate influences of interference signals by setting appropriate thresholds. The method for converting the point to point similarity measurement into measurement of waveforms of the points accurately identifies and detects the R wave crest of the electrocardiosignal. The method is applied to related electrocardiogram analytical instruments, can accurately identify the R wave crest in the electrocardiosignal, and is favorable for improving detection and analysis capabilities of electrocardiogram analytical equipment.

The invention discloses an electrocardiosignal denoising method based on morphology and EMD (empirical mode decomposition) wavelet threshold value. The electrocardiosignal denoising method comprises the following steps: firstly, the mathematical morphology filter method is adopted to perform primary filtering on an electrocardiosignal to filter baseline drift in the electrocardiosignal, then, the EMD method is adopted to decompose the obtained electrocardiosignal and obtain a noise dominant component and a useful signal dominant component through classification, the threshold value denoising method is adopted to perform secondary filtering on the electrocardiosignal, and finally, the EMD method is adopted to reconstruct the electrocardiosignal to obtain the noise-filtered electrocardiosignal. The invention has the following remarkable effects: the method is simple and easy to implement; the morphological filter method, the EMD method and the threshold de-noising method are combined in an organic manner; compared with the traditional denoising method, the electrocardiosignal denoising method can comprehensively and effectively remove electrocardiosignal noise, keeps the useful information in the electrocardiosignal, and provides more valuable reference for change of cardiac functions and diagnosis of cardiac diseases.

The invention discloses a continuous blood pressure measuring device based on pulse waves. The continuous blood pressure measuring device comprises a pulse wave signal collecting module, a pulse wave signal denoising module, a pulse wave waveform extraction module, a pulse wave feature parameter collecting module and a blood pressure prediction module. Errors during the estimation of diastolic pressure can be reduced by adopting a dicrotic wave feature parameter method; during the denoising processing, the baseline drift and the high-frequency noise are removed, so that the interference is effectively reduced; the pulse wave time domain features can be further obtained only through collecting one path of pulse wave signals and only through detecting the starting point of the pulse waves; the feature obtaining method is simple; the detection speed is high; the calculation quantity is small; and the waveform quality is high. A blood pressure regression model can be conveniently built; the systolic pressure and the diastolic pressure can be well predicted; the prediction errors are small; and the important basis can be provided for clinic diagnosis and family health care.

The invention discloses an electrocardiosignal data processing method. The electrocardiosignal data processing method comprises the following steps of (1) collecting data of electrocardiosignals; (2) carrying out preprocessing on the collected data of the electrocardiosignals, wherein baseline drift removal processing and de-noising processing are carried out on the collected electrocardiosignals; (3) decomposing the electrocardiosignals into single-cycle electrocardiosignal sets; (4) carrying out characteristic extraction, carrying out signal classification according to single-cycle electrocardiosignals after normalization and the level of similarity of curve signals, and choosing electrocardiosignals in the maximum class as a template; (5) detecting signal input, carrying out similarity comparison between the electrocardiosignals and a central database template to determine the identity. According to the single-leading electrocardiosignal data processing method, the mode of characteristic curve matching is adopted to carry out curve similarity comparative studies on electrocardiosignal sequences, and complex procedures for extracting characteristic points are avoided.

A baseline correcting method and system for digital flash pulses includes: using a multi-threshold voltage sampling method to perform time shaft sampling on flash pulse signals so as to obtain the time-threshold sampling points of flash pulse waveforms; selecting the time-threshold sampling points, using the shape features of the flash pulses to rebuild pulse waveforms and distinguish model parameters, and estimating the baseline drift mean value of the flash pulses accordingly; performing baseline correction on the flash pulse waveforms according to the base line drift mean value. The invention further discloses a baseline correcting system for the digital flash pulses. The system comprises a digital sampling module, a baseline drift calculation module and a baseline correcting module. By the method and the system the baseline drift of the flash pulses can be discriminated effectively and accurately, fast real-time self-adaptation baseline correction is achieved, and the signal to noise ratio of flash pulse data measuring results and stability of a data acquiring system are increased.

The invention relates to an electrocardio-signal emotion recognition method based on deep learning. The electrocardio-signal emotion recognition method comprises the following steps that under variousemotion picture inducing conditions, electrocardio data of a testee under different emotions is acquired through the chest or the wrist, the acquired electrocardio data is segmented into electrocardio signals fixed in length, and corresponding labels are made for the electrocardio data obtained under different emotions; the acquired electrocardio data is preprocessed, baseline drift removal basedon wavelet transformation is firstly performed, and noise removal is performed; data standardization is performed, normalization processing is conducted on noise-removed electrocardio data; a model training stage is performed.

The invention relates to the technical field of computer application, specifically, relates to a heart sign obtaining method and a heart sign obtaining device. The heart sign obtaining method comprises the steps of: obtaining photoplethysmography (PPG) data; performing low-pass filtering processing with cut-off frequency less than or equal to 4 HZ on the PPG value and removing high-frequency noise; performing discrete wavelet filtering processing on the PPG value subjected to high-frequency noise removal and correcting baseline drift; obtaining the wave crest and wave trough positions of PPG waveform according to the PPG data subjected to baseline drift correction, and obtaining the time interval value between the wave crests according to the wave crest and wave trough positions and the sampling rate of the PPG data; obtaining RR interval data according to the time interval value between the wave crests, and calculating the heart sign according to the RR interval data. By adoption of the method and the device, a user can comprehensively obtain the heart sign value.

The invention provides an electrocardiosignal R peak detection method based on waveform characteristic matching. The method utilizes waveform characteristic matching to identify R peak of electrocardiosignal, the characteristic matching method takes difference vector between points as basic characteristic, the basic characteristic has translational invariance and rotational invariance and can overcome influence of baseline drift of electrocardiosignal signal; meanwhile, logarithm polar coordinate transformation is carried out on the difference vector and partition is carried out to measure similarity of wave form, the measurement is sensitive to the adjacent morphological characteristic, can capture global outline information of wave form and has robustness to wave form ripple; besides, influence of interference signal can be eliminated by setting proper threshold, and further accurate identification and detection on R peak of electrocardiosignal are realized. The invention is applied to related electrocardiogram analyser, accurate identification on R peak of electrocardiosignal can be realized, thus being beneficial to improving detection and analysis capability of electrocardiogram analysis equipment.

The invention provides a measuring method for physiological parameters, and belongs to the technical field of physiological signals, and especially relates to a measuring method for physiological parameters. The invention provides a measuring method for physiological parameters, and the method is low in use cost and good in measuring effect. The method comprises the following steps: 1) a user finger covering a mobile phone camera, turning on a mobile phone flash lamp, and taking pictures by an image sensor in the mobile phone camera; 2) a mobile phone performing quantization determining on green ray value of RGB of all pixels in each frame of images, and calculating average gray value, to draw a green ray curve, calculating color variation of each frame, to form a pulse wave curve; performing median filtering on the pulse wave, and removing baseline drift; 3) inputting user body parameters; and finding a main wave crest, a dicrotic crest, the height of the main wave crest, the height of the dicrotic crest in the pulse wave which is processed by filtering, and calculating physiological parameters of a human body through the parameters.

The invention relates to electrocardio-beat feature automatic extraction and classification methods based on a deep learning method. The electrocardio-beat feature automatic extraction method comprises the following steps: (1) removing high-frequency noise and baseline drift by utilizing biorthogonal wavelet transform; (2) detecting R waves by utilizing maximum and minimum values generated by binary sample wavelet transform; and (3) detecting a QRS wave group and P and T waves on the basis of the R waves in step (2). Deep learning classification (heart beat learning classification) is carriedout on the detected wave form data information by utilizing a bi-directional long short-term memory (Bi-LSRM) network. The methods provided by the invention have the advantages that the feature extraction procedure is effectively simplified, the wave forms are accurately located, the electrocardio signals are accurately classified, etc.

The invention provides a fetus electrocardio blind separation method based on the relative sparsity of time domains of source signals, comprising the following steps of: firstly acquiring mutually overlaid mother-fetus mixed electrocardiosignals of mother and fetus electrocardio in two paths from the body surface of the abdomen of a mother; then carrying out pretreatment on the acquired mother-fetus mixed electrocardiosignals, wherein the pretreatment includes the steps of: correcting the baseline drift of the mother-fetus mixed electrocardiosignals, and filtering the interference of power frequency of 50 Hz, the interference of high-frequency myoelectric signals, and the like; respectively positioning the mother electrocardiosignal and the fetus electrocardiosignal in the pretreated mother-fetus mixed electrocardiosignals; then searching relatively sparse time slices of the mother electrocardio and the fetus electrocardio; and finally separating the mother electrocardiosignal from the fetus electrocardiosignal by utilizing a basic blind source separation algorithm resolved through the general features of a matrix. The invention solves the problem that the time domains and frequency domains of the mother electrocardiosignal and the fetus electrocardiosignal are mutually overlaid and difficult to separate and can efficiently and accurately extract the fetus electrocardiosignal used for medical diagnosis.

The invention discloses a method for identifying atrial fibrillation and atrial premature beats from a 10-second electrocardiogram. The method comprises the following steps that S1, firstly, an electrocardiogram signal is pretreated to filter out baseline drift, power frequency interference and other noise, the electrocardiogram signal after the noise is filtered out is re-sampled to a certain fixed sampling rate, then electrocardiogram QRS wave feature points are extracted by adopting a method based on a wavelet transformation and logic regression algorithm, and then a T wave is searched forby adopting a search window dynamically determined based on an instantaneous heart rate. The invention relates to the technical field of medical examination. The method for identifying atrial fibrillation and atrial premature beats from the 10-second electrocardiogram has the advantages that by defining a set of feature parameters based on heart rate change and a P wave, an obtained feature combination can identify atrial fibrillation and other heart rhythm better, the feature parameters are less sensitive to noise interference, a set of feature parameters based on the P wave are defined, theaccuracy is further improved, and quality information of the electrocardiogram signal is utilized, so that the identification of PAC is more accurate.

The invention discloses a heart rate variability feature classification method based on generalized scale wavelet entropy, and belongs to the field of electrocardiosignal processing. The method comprises the steps that after an original electrocardiosignal to be processed is preprocessed through interference and baseline drift removal, R wave positioning is performed, and an HRV sequence is obtained by calculating the interval between every two R waves; discrete wavelet transform is performed on the HRV sequence to obtain discrete wavelet coefficients, and then alpha-order generalized wavelet entropy of all layers of the wavelet coefficients is calculated by selecting appropriate alpha values as needed; scales with the statistical difference are screened out to serve as feature layers according to the obtained entropy values, and the alpha-order generalized wavelet entropy values of the feature layers are utilized to construct feature vectors to perform classification identifying on the electrocardiosignal.

A gas sensor array concentration detection method based on fuzzy division and model integration belongs to the field of a gas sensor array signal processing technology. According to the method, time division of baseline drift data is carried out by a fuzzy clustering method, and a source dataset is divided into multiple sub-datasets with different drift degrees; then, regression models of different training datasets are established to obtain several sub-regression models; an optimal weight set of each sub-regression model is obtained within the training dataset, and center of clustering and optimal weight undergo fitting to obtain an optimal weight fitting function; and during the test phase, fitting weight is calculated on the basis of the optimal weight fitting function and clustering center time, and the sub-regression models integrate forecasted results of data to be tested so as to obtain the final gas concentration value. By the method, a pattern recognition model can be changed adaptively, changes of drifting can be traced, the influence of drifting on concentration detection performance is effectively reduced, and long-term accuracy of concentration measurement is guaranteed.

The invention relates to a low-power consumption portable electrocardiograph monitor and a control method thereof, which belongs to the field of medical appliances. The portable electrocardiograph monitor comprises an electrocardiosignal collection unit, an electrocardiosignal analysis and storage unit and an upper computer. The control method comprises the following steps of: step 1: carrying out digital filtering to collected signals by using a wavelet entropy optimal threshold denoising method; step 2: carrying out rejection of baseline drift to the signals processed by step 1 by using a morphological filtering method; and step 3: carrying out disease classification to the signals processed by step 2 by using a support vector machine algorithm. The invention has the advantage of high integrity, and has the characteristics of portability, compactness and economy.

The invention discloses a double-layer morphological filter based electrocardiosignal preprocessing method. The method is characterized by including the method: firstly, utilizing a first-layer morphological filter constructed by triangular structure elements to process an original electrocardiosignal f0 so as to obtain a primary filter signal f1; secondly, processing the primary filter signal f1 by the aid of a second-layer morphological filter constructed by linear structural elements to obtain a secondary filter signal f2, and thirdly, comparing the primary filter signal f1 obtained in the first step to the secondary filter signal f2 obtained in the second step to acquire a difference so as to acquire a preprocessed electrocardiosignal f3. The method has the remarkable advantages that electrocardiosignals are processed by the aid of the double-layer morphological filters, on the basis of retaining useful characteristics of the electrocardiosignals, baseline drift in the electrocardiosignals is acquired accurately and is removed effectively by making difference, and accuracy rate of R wave detection during follow-up processing is improved beneficially.

The invention discloses a dynamic electrocardiogram (ECG) T wave alternate quantitative analysis method based on models, which belongs to the technical field of biomedical signal processing. The dynamic electrocardiogram (ECG) T wave alternate quantitative analysis method includes steps of preprocessing 12 lead ambulatory electrocardiograms of a patient at first and removing random disturbance including baseline drift, power frequency disturbance, myoelectricity noise and the like; building various channel electrocardiosignal state space models and realizing robust estimation to electrocardiosignal waveforms by the aid of a dynamic multi-scale state estimation theory; applying a multi-sensor data fusion method to realize T wave fusion extraction and realizing T wave quantitative description; and finally realizing quantitative analysis for T wave alternate signals according to the analytic function of T waves. The dynamic electrocardiogram T wave alternate quantitative analysis method has the advantages that on the basis of the electrocardiosignal state spatial models, T wave quantitative analysis is realized at first, then dynamic electrocardiogram T wave alternate real-time detection and analysis are realized, and accordingly the dynamic electrocardiogram T wave alternate quantitative analysis method is convenient for catching T wave alternate electrocardio abnormal conditions suddenly caused in daily life and increases detecting level and diagnosis ability to patients in danger of sudden cardiac death.

The invention provides a heart rate variability feature classification method based on multi-scale Renyi entropy and belongs to the field of electrocardiosignal processing. R wave positioning is performed after pretreatment such as interference and baseline drift removal is performed on to-be-processed original electrocardiosignals, and the interval of adjacent R waves is calculated to obtain an HRV sequence; discrete wavelet coefficients are obtained through discrete wavelet conversion of the HRV sequence; a proper q value is selected according to requirements to calculate the Renyi entropy of the wavelet coefficient of each layer; feature vectors are constructed by using the calculated Renyi entropy values of all scales for classification and identification of the electrocardiosignals.

The invention discloses an electrocardiographic signal de-noising method based on adaptive threshold wavelet transform. The method is characterized by comprising following steps: step 1: using the Mallat algorithm, the wavelet function sym6 and the number of decomposition layers J are selected, and the noisy ECG signal is decomposed by wavelet to obtain approximate coefficients and detail coefficients; step 2: setting the threshold for adaptive detail coefficients at each layer and selecting the threshold function; step 3: performing adaptive threshold processing on the detail coefficients ofeach layer, removing power frequency interference and myoelectric interference, and removing baseline drift by processing the approximation coefficients; step 4: performing wavelet reconstruction on the electrocardiographic signals after processing to obtain approximate optimal estimate value of signals. The method of the present invention makes full use of the multiresolution feature of the wavelet transform. An adaptive threshold selection method is provided. Different thresholds are used at each level to separate the noise and signal flexibly, improving the separability of signal characteristics; in the three aspects of visual, mean square error, and signal-to-noise ratio, the effect is better than the traditional method, and the detailed information of the image is retained better, which has higher practical value.

The invention discloses a baseline processing device that aims at signals containing pulses in uneven distribution and baselines in slow transformation, which comprises the following components: an A / D sampling unit used for sampling a counting signal and obtaining sample data, a baseline extracting unit used for inputting the sample data, and when the number of the input sample datum is equal to or more than N, the N sample datum are ordered by size according to the sampled order in each time, and any sample data A in the N sample datum with size equal to a mean value is output, wherein, the width of the N sample datum is much more than the width of a single pulse in a digital signal, less than the width of baseline drift, and more than two times of the width sum of all pulses in the distribution width, a phase compensating unit used for inputting the digital signal and outputting sample data B in the first-in-first-out order, the width of the phase compensating unit is M, which equals to a half of N, and a first subtractor used for inputting the sample data A and the sample data B, and outputting the result of the sample data B subtracted by the sample data A as baseline data.

The invention discloses a hyperspectral imaging light source system. An illumination box is a closed rectangular solid, wherein the cavity of the illumination box is respectively provided with a line scan camera, an optical splitting system, an object stage of an electric control translation stage, and an object to be detected, an electric control translation stage lead screw, a line source box and a photosensitive diode which are arranged on the object stage; a line source controller, an electric control translation stage controller and a computer are arranged outside the illumination box; and the photosensitive diode senses the light-intensity variation of the line source and inputs feedback signals to the line source controller, and a halide lamp is arranged in the line source box and is connected with the line source controller. The invention does not need to be subject to the conduction through optical fibers, thereby eliminating noises caused by the nonuniform illumination and improving the illumination uniformity and stability. Besides, the invention eliminates the light-intensity variation as a result of the increased service life of the light source and the instability of the external circuit, inhibits the baseline drift phenomenon in the signal acquisition process, improves the utilization ratio of the light source, and can acquire hyperspectral images of high quality and high stability.

The invention discloses a method for diagnosing a fault of an automatic ammunition supply and transportation device, and aims to realize low maintenance cost and short period. The method comprises the following steps of: arranging measurement points on the large-aperture artillery automatic ammunition supply and transportation device, and selecting driving current of an ammunition supply motor and a hydraulic oil source motor as the measurement points according to a driving mode supplied by an automatic ammunition supply and transportation motion cyclic graph; simultaneously acquiring and recording angular displacement signals and angular speed signals of a rotary ammunition cabin, a coordination arm, a turning mechanism and an ammunition transportation mechanism as well as load current change signals of driving motors by a portable signal acquirer; performing signal data correction work of baseline drift correction and wild point removal on the acquired and recorded angular motion signals and current change signals; performing time domain analysis on an acquired acceleration impact signal and an acquired current impact signal, and making an amplitude probability density and probability distribution curve; and performing short-time-frequency characteristic extraction and small sample signal analysis on an impact response signal, analyzing a wavelet energy spectrum, and supplying peak values and frequencies in a frequency domain, and energy and entropy of each frequency band.

The invention discloses a method and a device for identifying artifact electrocardiograph waveforms. The method comprises the following steps of: reading the original electrocardiograph waveform data; filtering the middle baseline drift and high-frequency noise in the original electrocardiograph waveform data, and then obtaining the preprocessed electrocardiograph waveform data; estimating the abnormal electrocardiograph waveform data of the preprocessed electrocardiograph waveform data to obtain the normal electrocardiograph waveform data; obtaining the data corresponding to a section of continuous low-error electrocardiograph waveform in the normal electrocardiograph waveform data, extracting the electrocardiograph waveform data corresponding to an RR period from the data corresponding to the continuous low-error electrocardiograph waveform, and adopting the data as the electrocardiograph template data; adopting the electrocardiograph waveform data, which corresponds to the RR period in the normal electrocardiograph waveform data of which residual energy is larger than the pre-set multiple of the residual energy of the electrocardiograph template data, as the artifact electrocardiograph waveform data. The method can quickly identify the artifact electrocardiograph waveforms. In addition, the invention further provides a device for identifying artifact electrocardiograph waveforms.

The invention provides a method for detecting the heart rate by utilizing an intelligent mobile phone camera. The method comprises the steps that 1, a user covers the camera with a finger, and the camera continuously acquires images; 2, gray information in each frame of image is extracted, the gray information of every frame of image is accumulated for serving as the overall gray value of every frame of image, and the overall gray values of the continuous image form a blood gray change waveform; 3, preprocessing, baseline drift removing and smooth denoising are carried out on the waveform; 4, peak values of the waveform are extracted, and the number of frames between every two adjacent peak values is determined for calculating the real-time heart rate; 5, the extracted waveform signal is zoomed for simulating a dynamic electrocardiogram. The method does not need the aid of any other medical equipment, and only the finger covers the camera, and the electrocardio waveform can be simulated, so that the real-time heart rate can be calculated.

The invention relates to an intelligent method for testing cardiograms by basing on quanta a simple recursion neural network, which comprises the steps that: the cardiac electric signals are sent to a self-adaptation power frequency disturbance-restriction module for restricting the disturbance of the power frequency; the two output cardiac electric signals in the previous step are sent to a blind baseline drift disturbance-restriction module so as to remove the breath baseline drift disturbance; the output cardiac electric signals in the second step are sent to the quanta simple recursion neural network for carrying out cardiac electric intellectual test and output. The self-adaptation power frequency disturbance-restriction module comprises two same groups of self-adaptation power frequency disturbance-restriction units, and carries out power frequency disturbance-restriction on the two collected groups of cardiac electric signals simultaneously and respectively. The self-adaptation power frequency disturbance-restriction module can automatically restrict power frequency disturbance by adopting the cardiac electric power frequency disturbance-restriction method basing on QR decomposing recursive least squares (RLS) algorithm self-adaptation trap technology. The invention can not only realize cardiac electric intellectualization test, but also remove the power frequency and the breath baseline drift disturbance in the effective frequency band of cardiac electric signals, and solve the difficulty that digital filtration method eliminates the disturbance of aliasing in spectrum.

An embodiment of the invention is an organic thin film transistor chemical sensor. The sensor includes a substrate. A gate electrode is isolated from drain and source electrodes by gate dielectric. An organic ultra-thin semiconductor thin film is arranged with respect to the gate, source and drain electrodes to act as a conduction channel in response to appropriate gate, source and drain potentials. The organic ultra-thin film is permeable to a chemical analyte of interest and consists of one or a few atomic or molecular monolayers of material. An example sensor array system includes a plurality of sensors of the invention. In a preferred embodiment, a sensor chip having a plurality of sensors is mounted in a socket, for example by wire bonding. The socket provides thermal and electrical interference isolation for the sensor chip from associated sensing circuitry that is mounted on a common substrate, such as a PCB (printed circuit board). A method of operating an organic thin film transistor chemical sensor exposes the sensor to a suspected analyte. A low duty cycle voltage pulse train is applied to the gate electrode to reduce baseline drift while sensing for a conduction channel change.