System for the early detection of sepsis using continuous multiparameter patient monitoring and predictive clinical modeling

Abstract

A system for the early detection of sepsis by means of continuous multiparametric patient monitoring and predictive clinical modeling, the system comprising: a housing designed to be attached to or placed near a patient; a plurality of physiological sensor units arranged within or connected to the housing and configured to continuously acquire physiological signals, including cardiac activity, respiration, temperature, blood oxygen saturation, and blood pressure; a signal conditioning unit electrically connected to each of the numerous physiological sensor units and configured to perform the amplification, filtering, and analog-to-digital conversion of the acquired physiological signals; and a processing unit operationally connected to the signal conditioning unit.a storage unit operationally connected to the processing unit; a communication unit configured to transmit data to external devices; and an alarm generation unit whose processing unit is configured to continuously receive digitized physiological data, perform preprocessing including noise reduction and baseline normalization, extract multi-parameter features, perform a temporal trend analysis across sequential data segments, generate a composite sepsis risk score based on an integrated analysis of the extracted features, and activate the alarm generation unit when the composite sepsis risk score exceeds a predefined or dynamically adjusted threshold.

Description

Technical field of the invention The present invention relates generally to biomedical engineering, monitoring systems for intensive care medicine, and computer-aided technologies for clinical decision support. In particular, the invention relates to a system with a structured patient monitoring device equipped with continuous physiological multiparameter sensors and predictive clinical modeling for the early detection of sepsis in inpatients or outpatients. BACKGROUND OF THE INVENTION Sepsis is a life-threatening condition resulting from a dysregulated immune response to infection, and if left untreated, it often leads to organ failure and death. Conventional diagnostic methods rely on sporadic clinical observations, laboratory tests, and the treating physician's assessment, which can lead to delayed diagnosis due to short measurement intervals and subjective interpretation. Common monitoring devices typically record individual physiological parameters such as heart rate, temperature, or blood pressure, but lack the integrated analytical capability to correlate multiple parameters over time and predict impending septic events. Furthermore, current monitoring systems in hospitals often operate reactively, only triggering alarms when a patient's condition has clinically deteriorated significantly. The lack of continuous, synchronized, and predictive analysis of physiological signals limits the ability to detect early-stage sepsis, which is characterized by subtle deviations in various parameters. Therefore, there is a need for a system that integrates a physical patient monitoring structure with real-time multi-parameter acquisition and predictive modeling, enabling the identification of early sepsis signatures before the onset of clinical symptoms. Sepsis remains one of the leading causes of death in intensive care units and emergency departments worldwide. It is characterized by a dysregulated immune response to infection, which can rapidly lead to organ failure, septic shock, and death. The clinical challenge of sepsis lies not only in its severity but also in its heterogeneous symptomatology and dynamic course. In its early stages, sepsis often manifests with subtle physiological abnormalities such as mild tachycardia, slight temperature fluctuations, or mild respiratory distress, which are clinically indistinguishable from non-critical conditions. Therefore, timely detection using conventional diagnostic methods is difficult, and delayed intervention significantly increases morbidity and mortality rates.The need for early detection systems has driven the development of various monitoring and analysis technologies; however, existing solutions still have significant technical and clinical limitations. Traditional sepsis diagnosis relies heavily on intermittent clinical assessments, including manual measurement of vital signs such as heart rate, respiratory rate, blood pressure, and temperature, combined with laboratory tests such as blood cultures, lactate levels, and inflammatory markers. These approaches are inherently episodic, providing only snapshots of a patient's physiological state rather than continuous insights into dynamic changes. The time gaps between measurements can lead to missed early warning signs, particularly in rapidly deteriorating patients. Furthermore, laboratory-based diagnostic procedures are often associated with delays due to sample collection, processing, and analysis, which can delay clinical decision-making.Because of these limitations, traditional approaches are reactive rather than proactive; interventions are typically initiated only after a significant physiological deterioration. To address these shortcomings, early warning systems integrated into electronic health records have been developed, such as the criteria for systemic inflammatory response syndrome (SIRS), the SOFA score (Sequential Organ Failure Assessment), and the SOFA Quick Score. These systems attempt to quantify the risk of sepsis based on predefined thresholds of physiological parameters and clinical observations. However, these assessment mechanisms are primarily rule-based and use static thresholds that do not account for patient-specific differences or temporal trends. For example, a fixed heart rate threshold may not detect abnormalities in patients with chronic cardiovascular disease.Furthermore, due to their sensitivity to nonspecific physiological changes, these systems often exhibit high false-positive rates, leading to alarm fatigue among physicians and reduced trust in automated alerts. Conversely, they can also miss early sepsis in patients whose physiological changes, while within the nominal threshold ranges, show abnormal trends over time. Advances in patient monitoring technology have led to the development of patient monitors capable of continuously recording vital parameters such as ECG signals, oxygen saturation, and blood pressure. While these devices provide high-resolution physiological data, their primary function remains data display and threshold-based alerting, rather than integrated predictive analytics. Most existing monitors operate independently and are unable to correlate multiple physiological parameters or analyze their interactions over time. This isolated approach limits the detection of complex patterns indicative of incipient sepsis, which often result from subtle interactions between variables such as heart rate variability, respiratory dynamics, and perfusion changes.Furthermore, these systems typically generate alarms based on momentary threshold exceedances, which may not reflect clinically relevant trends and thus contribute to excessive false alarms and reduced clinical utility. In recent years, computer-aided approaches using machine learning and artificial intelligence have been introduced to improve sepsis prediction. These systems use historical patient data to train predictive models that can identify patterns associated with sepsis onset. While such approaches demonstrate improved predictive performance in controlled settings, they face several practical challenges in real-world applications. A key limitation is their reliance on high-quality, well-annotated datasets for training, which may not be representative of diverse patient populations. Models trained on specific cohorts may exhibit lower generalizability when applied to other clinical settings or demographic groups.Furthermore, many machine learning models function as black-box systems, limiting the interpretability of their predictions. This lack of transparency can hinder physician acceptance and complicate clinical decision-making, particularly in critical care settings where understanding the reasons for a warning is essential. Another significant drawback of existing predictive systems lies in their reliance on retrospective data analysis rather than true real-time processing. Many implementations analyze data packets at predefined intervals, leading to detection delays and reduced system responsiveness. Furthermore, integration with existing hospital infrastructure remains a challenge, as data from various sources, such as patient monitors, laboratory systems, and electronic health records, is often fragmented and stored in incompatible formats. This lack of interoperability necessitates complex data aggregation and preprocessing pipelines, which can introduce additional delays and potential sources of error. Wearable health monitoring devices have also been explored as a means of providing continuous physiological monitoring outside of traditional clinical settings. These devices typically include sensors for measuring heart rate, temperature, and activity, and are designed for portability and patient comfort. However, most wearable devices are optimized for general health monitoring rather than clinical diagnostics. They often exhibit limited measurement accuracy, susceptibility to motion artifacts, and a restricted range of parameters. Furthermore, wearable devices generally lack built-in predictive modeling capabilities; instead, they rely on external platforms for data analysis. The lack of robust, real-time analysis within the device limits their effectiveness in detecting acute conditions such as sepsis. Another category of existing solutions comprises centralized monitoring systems in hospitals, where data from multiple patients are aggregated and analyzed in a central control room. While these systems enable remote monitoring and centralized alarm management, they are highly dependent on network infrastructure and continuous data transmission. Network latency, data loss, or connectivity issues can compromise monitoring reliability and delay critical alarms. Furthermore, centralized systems often struggle to provide patient-specific contextual analysis because they frequently rely on generalized models that do not dynamically adapt to individual patient values ​​or changing physiological states. Another limitation of many existing solutions lies in their insufficient consideration of temporal dynamics and longitudinal data. The course of sepsis is inherently time-dependent, with early indicators often manifesting as gradual trends rather than abrupt changes. Systems based on snapshots or short-term averages may fail to capture these trends, thus missing opportunities for early intervention. Furthermore, many current approaches do not incorporate adaptive learning mechanisms that update predictive models based on incoming patient data, thereby limiting their ability to improve predictions over time. Besides technical challenges, practical aspects such as device ergonomics, patient comfort, and ease of implementation also influence the effectiveness of sepsis detection systems. Bulky or cumbersome monitoring devices can restrict patient mobility and reduce compliance, especially outside of intensive care units. Frequent recalibration, maintenance requirements, and the complexity of operation also limit the scalability of existing systems. Despite significant advances in monitoring technologies and predictive analytics, current sepsis detection solutions still have limitations, including intermittent data acquisition, the use of static thresholds, the lack of multi-parameter integration, limited real-time processing capabilities, poor interoperability, a high false alarm rate, and insufficient personalization. These shortcomings hinder the reliable early detection of sepsis in diverse clinical settings. Therefore, there is an urgent need for a system that integrates continuous multi-parameter physiological monitoring with predictive real-time clinical modeling in a unified, structured device, enabling adaptive, patient-specific analyses and timely alert generation. SUMMARY OF THE INVENTION The present invention relates to a system for the early detection of sepsis. It comprises a physically integrated monitoring device that can be attached to or positioned near the patient. The device includes several physiological sensors, a signal acquisition circuit, a processing unit, a memory, and communication interfaces. The system continuously acquires physiological data of various parameters, including heart rate variability, respiratory rate, blood oxygen saturation, skin temperature, blood pressure trends, and perfusion indices. The processing unit performs predictive clinical modeling procedures that analyze temporal patterns, correlations between parameters, and deviation metrics to generate a risk score for early sepsis. The system also includes a housing that supports ergonomic sensor placement and ensures stable signal acquisition under varying patient conditions. Predictive modeling integrates statistical trend analysis, machine learning for classification, and threshold-adaptive evaluation mechanisms for early sepsis detection. The system is configured to generate alerts, transmit risk indicators to clinical interfaces, and enable continuous monitoring without interrupting patient care. The present invention aims to provide a sepsis early detection system that integrates a structurally defined patient monitoring device with continuous physiological multiparameter measurement and predictive real-time clinical modeling. This enables the timely detection of sepsis before the onset of severe clinical symptoms. The invention overcomes the limitations of intermittent and threshold-based monitoring by ensuring uninterrupted acquisition and analysis of physiological data within a unified device architecture. A further objective of the invention is to provide a monitoring system that can simultaneously acquire and synchronize several physiological parameters, including cardiac activity, respiratory patterns, temperature changes, perfusion characteristics, and hemodynamic indicators. This allows for the effective analysis of correlations between the parameters and combined deviations in order to detect pathological changes associated with sepsis at an early stage. The invention further aims to ensure highly precise signal acquisition through integrated sensor units and signal processing circuits in a compact and ergonomically designed housing. A further objective of the invention is to provide a system with a processing unit for the continuous preprocessing, feature extraction, and analysis of temporal trends in physiological data. This enables the detection of subtle and progressive deviations from patient-specific baseline values. The invention aims to facilitate a dynamic assessment of physiological variability instead of static threshold values, thereby improving sensitivity and specificity in the early detection of sepsis. A further objective of the invention is to provide a predictive clinical modeling mechanism that generates a composite sepsis risk score based on weighted contributions from several physiological parameters. The weighting factors are adaptively adjusted to patient-specific characteristics and changing physiological states. The invention aims to employ advanced analytical methods, including statistical modeling and machine learning, to improve predictive accuracy and reduce false-positive and false-negative results. A further objective of the invention is to provide a system for real-time processing and continuous updating of predictive results. This minimizes detection latency and ensures that clinicians always have access to up-to-date risk assessments that reflect the patient's current condition. The invention aims to enable proactive clinical intervention by generating early warnings before any manifest clinical deterioration occurs. Another objective of the invention is to provide a communication interface within the device structure, configured to transmit physiological data, predictive outputs and alarm signals to external clinical systems, including hospital information systems and physician-operated devices, thereby enabling seamless integration into existing healthcare infrastructures and providing remote monitoring functions. A further objective of the invention is to provide an alarm generation mechanism configured to issue multimodal notifications, including visual, audible, and digital alarms, when the calculated sepsis risk score exceeds predefined or dynamically adjusted thresholds. This ensures timely awareness and response by healthcare providers while simultaneously reducing alarm fatigue through intelligent alarm prioritization. Another objective of the invention is to provide a structurally robust and ergonomically designed device housing that enables stable placement of the sensor units on or near the patient's body, thus ensuring consistent signal acquisition under various clinical conditions, while maintaining patient comfort and minimizing disturbances caused by motion artifacts or environmental influences. A further objective of the invention is to provide a system with integrated memory for storing both real-time physiological and historical data. This enables longitudinal analyses and the continuous improvement of predictive models based on the collected patient-specific information. The invention aims to support adaptive learning and improve predictive accuracy over extended monitoring periods. Another objective of the invention is to provide a scalable and deployable solution that can be used in various clinical settings, including intensive care units, general wards, emergency departments and outpatient facilities, thereby extending the benefits of early sepsis detection beyond specialized care units. Accordingly, the invention aims to provide a comprehensive and integrated system that combines a physical monitoring device with advanced predictive analytics, thereby ensuring continuous, accurate and early detection of sepsis, while addressing the technical and clinical limitations of existing monitoring and diagnostic procedures. BRIEF DESCRIPTION OF THE IMAGE These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. 1 shows a block diagram of a system for the early detection of sepsis by means of continuous multiparameter patient monitoring and predictive clinical modeling. Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only those specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention. It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation thereof. References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment. The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components. Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting. Embodiments of the present disclosure are described in detail below with reference to the attached drawing. Fig. 1 shows the block diagram of a system for the early detection of sepsis using continuous multiparameter patient monitoring and predictive clinical modeling. The system 100 comprises: a housing (102) for attachment to or placement near the patient; several physiological sensors (104) arranged in or connected to the housing, which continuously acquire physiological signals such as cardiac and respiratory activity, temperature, blood oxygen saturation, and blood pressure; a signal conditioning unit (106) electrically connected to each sensor, which amplifies, filters, and converts the acquired physiological signals from analog to digital; a processing unit (108) connected to the signal conditioning unit; a memory (110) connected to the processing unit; and a communication unit (112) for data transmission to external devices.and an alarm generation unit whose processing unit is configured to continuously receive digitized physiological data, perform preprocessing including noise reduction and baseline normalization, extract features with multiple parameters, perform an analysis of the temporal trend across sequential data segments, generate a composite sepsis risk score based on an integrated analysis of the extracted features, and activate the alarm generation unit when the composite sepsis risk score exceeds a predefined or dynamically adjusted threshold. In one embodiment, the structural housing (102) comprises a biocompatible covering with a contoured surface adapted to a body region of the patient and further comprises fastening elements such as adjustable straps or adhesive surfaces configured to ensure stable positioning of the plurality of physiological sensor units during the movement of the patient. In one embodiment, the electrocardiographic sensor unit is configured to detect electrical heart signals, and the processing unit is configured to derive heart rate variability parameters, including time and frequency domain indices, for inclusion in the composite sepsis risk score. In one embodiment, the photoplethysmographic sensor unit (104) is configured to detect optical signals corresponding to changes in blood volume, and the processing unit is configured to calculate perfusion indices and variability metrics of oxygen saturation based on the detected optical signals. In one embodiment, the respiratory sensor unit is configured to detect the variability of the respiratory rate and breathing pattern using impedance-based or motion-based sensors, and the processing unit is configured to detect irregular respiratory trends that indicate an incipient physiological deterioration. In one embodiment, the thermometric sensor unit is configured to measure skin temperature and estimate core body temperature gradients, and the processing unit is configured to calculate temperature deviation trends relative to a patient-specific initial value stored in the memory unit. In one embodiment, the signal processing unit (106) comprises a plurality of dedicated analog input stages, each assigned to a physiological sensor unit. Each analog input stage has programmable gain and adaptive filtering for reducing motion artifacts and ambient noise. In one embodiment, the processing unit (108) is further configured to perform a sliding window analysis over continuously acquired physiological data, wherein each window corresponds to a predefined time period and overlapping windows are used to capture the temporal evolution of physiological parameters. In one embodiment, the processing unit (108) is configured to calculate correlation metrics between parameters representing relationships between cardiac, respiratory, temperature and perfusion signals, and to incorporate these correlation metrics into the composite sepsis risk score. In one embodiment, the processing unit (108) is configured to assign weighting coefficients to each physiological parameter, the weighting coefficients being dynamically adjusted based on patient-specific baseline features stored in the memory unit and updated in real time according to observed physiological trends. The described system is implemented using tangible, physical components that work together to perform real-time physiological monitoring and risk assessment. The housing is made of biocompatible material and contains several sensor elements positioned in direct contact with the patient's body. Each physiological sensor unit is a dedicated hardware sensor that captures specific biological signals, such as electrodes for measuring cardiac activity, optical transmitters and receivers for measuring blood oxygen saturation, pressure transducers for measuring blood pressure, thermosensors for measuring temperature, and motion or impedance sensors for measuring respiration. The signal processing unit consists of analog electronics with amplifiers, filters, and analog-to-digital converters that transform the raw sensor data into usable electrical signals.The processing unit is implemented as an integrated circuit that performs predefined electrical operations on incoming data. The storage unit consists of physical data storage elements that store basic physiological information and intermediate results. The communication unit includes hardware for wireless or wired transmission, enabling data exchange with external devices. The alarm generation unit is implemented using haptic output devices such as visual indicators, audible alarms, or haptic actuators that are physically triggered when electrical conditions corresponding to an increased risk are met. The present invention relates to a system for the early detection of sepsis. This system is integrated into a structurally integrated patient monitoring device that continuously acquires, processes, and analyzes physiological data of various parameters. For this purpose, a predictive clinical modeling procedure is executed by a processing unit. The system's operation is based on a sequential, but continuously adaptive, computational method in which the raw physiological signals are converted into clinically meaningful indicators and subsequently combined to provide an overall risk assessment for sepsis. During operation, the physiological sensor units integrated into or coupled to the housing continuously acquire analog signals corresponding to electrical cardiac activity, respiratory motion / impedance, blood oxygen saturation (measured optically), temperature (measured thermometrically), and blood pressure and perfusion parameters. These analog signals are transmitted to the signal processing unit, where they are amplified by programmable gain stages to ensure sufficient signal strength. Adaptive filtering then removes baseline deviations, motion artifacts, and environmental influences. The filtered analog signals are then digitized using high-resolution analog-to-digital conversion to generate time-synchronized time-series data streams with a common time reference. The digitized physiological data are received by the processing unit, which initiates a preprocessing phase to standardize and normalize the incoming signals. This preprocessing includes removing outliers using statistical filtering, interpolating missing measurements to ensure continuity, and normalizing relative to the baseline values ​​determined during a calibration phase. The processing unit maintains a dynamic baseline profile for each physiological parameter, which is stored in memory and continuously updated using weighted averaging. This prioritizes recent data while preserving long-term trends. After preprocessing, the processing unit extracts features from each physiological signal. For cardiac signals, it determines the intervals between heartbeats and calculates variability measures that reflect autonomic nervous system activity. For respiratory signals, it calculates respiratory rate, variability, and irregularity indices that indicate physiological stress. For photoplethysmographic signals, it extracts amplitude, waveform morphology, and perfusion-related features, including oxygen saturation variability. Temperature signals are analyzed to determine gradients and deviations from baseline, while pressure-related signals are processed to estimate trends in blood pressure and perfusion stability. These extracted features are structured into a multidimensional feature set that represents the patient's current physiological state. The processing unit then performs a temporal analysis by segmenting the feature data into overlapping time windows of predefined duration. Within each window, statistical measures such as mean, variance, rate of change, and higher-order trend indicators are calculated. The use of overlapping windows ensures the continuity of the trend analysis and allows the system to detect gradual physiological changes that might not be apparent in single measurements. Additionally, the processing unit calculates correlation metrics between parameters by evaluating the relationships between different physiological features, such as the coupling between heart rate variability and respiratory patterns or the relationship between temperature increases and perfusion changes.These correlation metrics provide information about systemic physiological reactions and are crucial for the early detection of sepsis, which often manifests as coordinated deviations of several parameters. The predictive clinical modeling technique implemented by the processing unit uses the extracted features and temporal descriptors to generate an overall sepsis risk score. Each physiological parameter contributes to this score via a weighting mechanism. The weighting coefficients are assigned based on the parameter's relative importance in indicating sepsis-related changes. These weighting coefficients are not static but are dynamically adjusted using adaptive logic. This logic considers patient-specific baseline characteristics, stored historical trends, and the rate of change of each parameter. For example, a rapid increase in respiratory rate can be assigned a higher weight if it is accompanied by a decrease in oxygen saturation and altered heart rate variability. The composite sepsis risk score is calculated as an aggregated representation of weighted deviations of each parameter from its baseline value, combined with correlation-based indicators and factors for temporal trends. The processing unit further refines this score through a predictive classification procedure, comparing the current set of features with stored patterns representing normal, risk, and septic physiological states. This comparison can be performed using trained computer models that evaluate nonlinear relationships between features and assign a probability measure corresponding to the likelihood of sepsis occurring within a given time period. To ensure reliability and reduce false alarms, the procedure incorporates a persistence check mechanism. According to this mechanism, the processing unit verifies whether anomalous trends persist across multiple consecutive time windows before confirming a significant change in the patient's condition. This prevents transient anomalies or noise-induced fluctuations from triggering unnecessary alarms. Additionally, the procedure includes anomaly detection logic that compares real-time feature values ​​with historical baseline distributions and identifies deviations that exceed adaptive thresholds based on statistical confidence intervals. The processing unit continuously updates the combined sepsis risk score at predefined intervals, ensuring near real-time responsiveness. Each updated score, along with its associated feature data, is stored in memory, enabling longitudinal analyses and incremental improvements in predictive accuracy. The system also stores previous risk scores, allowing the processing unit to calculate the rate of risk change over time. This serves as an additional indicator of rapid physiological deterioration. If the combined sepsis risk score exceeds a predefined or dynamically adjusted threshold, the processing unit activates the alarm generation unit. This unit generates multimodal notifications, including visual indicators on the device, audible signals, and digital alerts, which are transmitted via the communication unit to external systems such as physician monitoring devices or hospital information systems. The priority and intensity of the alerts are adjusted based on the level of the risk score and its rate of increase to differentiate between critical and moderate-risk conditions. The communication unit enables the continuous transmission of raw and processed data to external platforms, thus facilitating remote monitoring and integration into clinical workflows. Transmitted data includes real-time physiological signals, extracted features, risk assessments, and alarm status. The system ensures data synchronization and integrity through time-stamped data packets and error-checking mechanisms. The system also includes an adaptive learning function that uses historical patient data from memory to refine baseline profiles and adjust weighting coefficients over time. As data availability increases, the system improves its sensitivity to patient-specific physiological patterns, thereby increasing predictive accuracy and reducing false-positive results. This adaptive behavior enables the system to be used effectively with different patient groups and various clinical conditions. The system operates continuously and autonomously, requiring only minimal manual intervention after installation. By integrating real-time multiparameter monitoring with advanced predictive modeling, the invention enables the early detection of sepsis through the detection of subtle, coordinated physiological changes. This facilitates timely clinical intervention and improves patient outcomes. The present invention describes a system in the form of a structured patient monitoring device for the continuous acquisition and predictive analysis of physiological parameters to enable early detection of sepsis. The system comprises a housing made of biocompatible material that accommodates several embedded sensors and electronic circuits. The housing is dimensioned so that it can be attached to the patient's torso, limbs, or bed, thus ensuring consistent sensor contact and minimal motion-induced artifacts. The sensor units comprise an electrocardiographic sensor unit for recording electrical cardiac activity and deriving heart rate variability parameters, a photoplethysmographic sensor unit for measuring blood oxygen saturation and peripheral blood flow, a thermometric sensor unit for monitoring skin and core body temperature gradients, a pressure sensor unit for measuring blood pressure using cuff-based or cuffless methods, and a respiratory sensor unit for recording respiratory rate and breathing patterns. Each sensor unit is electrically connected to a signal processing circuit that includes amplification, filtering, and analog-to-digital conversion stages to ensure highly accurate signal acquisition. The system also includes a central processing unit (CPU) within the housing, which is connected to the sensor units. The CPU receives digitized physiological signals and performs preprocessing steps such as noise reduction, baseline correction, and normalization. The processed signals are stored in a memory that holds both short-term buffers and long-term trend data for temporal analysis. The central processing unit performs a predictive clinical modeling procedure that integrates multi-parameter data streams to detect early indicators of sepsis. The procedure calculates derived features such as heart rate variability indices, respiratory variability, temperature deviation gradients, perfusion variability, and composite hemodynamic stability metrics. The system performs temporal trend analysis by applying sliding window calculations across sequential data segments to identify progressive deviations from patient-specific baseline values. The predictive model also includes a weighted scoring mechanism in which each physiological parameter contributes to an overall sepsis risk score. This score is based on dynamically adjusted weighting coefficients. These coefficients are adaptively updated based on patient-specific characteristics and changing physiological patterns. The system applies classification logic to the calculated risk score, using predefined thresholds and probabilistic models to determine the probability of a sepsis outbreak. In an advanced implementation, the central processing unit uses machine learning to predict patient conditions. Historical patient data and real-time inputs are processed using trained models to categorize patients' conditions as "normal," "at risk," or "septic." The model considers interactions between features, nonlinear relationships, and temporal dependencies to improve predictive accuracy. The processing unit continuously updates the predictive output at defined intervals to reflect the current patient condition. The system includes a communication interface for transmitting the calculated sepsis risk score, alerts, and physiological data to external monitoring systems, hospital information systems, or clinician devices. The interface supports wired and wireless protocols, enabling seamless integration into clinical environments. The system also includes an alert unit that issues visual, audible, or digital alerts when the calculated risk score exceeds predefined thresholds. The device's design ensures stable sensor positioning through adjustable mounting mechanisms such as straps or adhesive pads and incorporates shielding features to minimize electromagnetic interference. The housing features thermal management to guarantee the operational stability of the electronic components during continuous use. During operation, the system continuously records the patient's physiological data, processes it using a predictive clinical modeling method, and generates a sepsis risk score that indicates early pathological changes. The system enables proactive clinical intervention by providing early warnings of severe symptoms, thereby improving treatment outcomes and reducing sepsis-related mortality. The invention thus provides an integrated system that combines a physical monitoring device with advanced predictive analytics and enables continuous, proactive real-time detection of sepsis through physiological multi-parameter monitoring and computer-aided modeling. The present invention relates to the field of biomedical instrumentation, patient monitoring systems, and computer-aided clinical decision support. In particular, the invention relates to a system in the form of a structured patient monitoring device configured for the continuous acquisition of physiological data with multiple parameters and for performing predictive clinical modeling for the early detection of sepsis in inpatient and outpatient settings. The drawing and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims. The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 A system for the early detection of sepsis using continuous multiparametric patient monitoring and predictive clinical modeling. 102 Structural design 104 Multiple physiological sensor units 106 Signal processing unit 108 Processing unit 110 Storage unit 112 Communication unit

Claims

A system for the early detection of sepsis using continuous multiparametric patient monitoring and predictive clinical modeling, the system comprising: a housing designed to be attached to or placed near a patient; a plurality of physiological sensor units arranged within or connected to the housing and configured to continuously acquire physiological signals, including cardiac activity, respiration, temperature, blood oxygen saturation, and blood pressure; a signal conditioning unit electrically connected to each of the numerous physiological sensor units and configured to perform amplification, filtering, and analog-to-digital conversion of the acquired physiological signals; and a processing unit operationally connected to the signal conditioning unit.a storage unit operationally connected to the processing unit; a communication unit configured to transmit data to external devices; and an alarm generation unit whose processing unit is configured to continuously receive digitized physiological data, perform preprocessing including noise reduction and baseline normalization, extract multi-parameter features, perform a temporal trend analysis across sequential data segments, generate a composite sepsis risk score based on an integrated analysis of the extracted features, and activate the alarm generation unit when the composite sepsis risk score exceeds a predefined or dynamically adjusted threshold. System according to claim 1, wherein the structural housing comprises a biocompatible covering with a contoured surface that adapts to a body region of the patient, and further comprises fastening elements, including adjustable straps or adhesive surfaces configured to ensure stable positioning of the plurality of physiological sensor units during the movement of the patient. System according to claim 1, wherein the electrocardiographic sensor unit is configured to detect electrical heart signals, and the processing unit is configured to derive heart rate variability parameters including time and frequency domain indices for inclusion in the composite sepsis risk score. System according to claim 1, wherein the photoplethysmographic sensor unit is configured to detect optical signals corresponding to changes in blood volume, and the processing unit is configured to calculate perfusion indices and variability metrics of oxygen saturation based on the detected optical signals. System according to claim 1, wherein the respiratory sensor unit is configured to detect the variability of the respiratory rate and breathing pattern by means of impedance-based or motion-based sensing, and the processing unit is configured to detect irregular respiratory trends that indicate an incipient physiological deterioration. System according to claim 1, wherein the thermometric sensor unit is configured to measure skin temperature and estimate core body temperature gradients, and the processing unit is configured to calculate temperature deviation trends relative to a patient-specific baseline stored in memory. System according to claim 1, wherein the signal processing unit comprises a plurality of dedicated analog input stage circuits, each of which is assigned to a physiological sensor unit, wherein each analog input stage circuit includes programmable amplification and adaptive filtering to reduce motion artifacts and ambient noise. System according to claim 1, wherein the processing unit is further configured to perform a sliding window analysis over continuously acquired physiological data, wherein each window corresponds to a predefined time period and overlapping windows are used to capture the temporal evolution of physiological parameters. System according to claim 1, wherein the processing unit is configured to calculate correlation metrics between parameters representing relationships between cardiac, respiratory, temperature and perfusion signals, and incorporates these correlation metrics into the composite sepsis risk score. System according to claim 1, wherein the processing unit is configured such that weighting coefficients are assigned to each physiological parameter, wherein the weighting coefficients are dynamically adjusted on the basis of patient-specific baseline features stored in the memory unit and updated in real time according to observed physiological trends.

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