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Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user’s sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.
Sleep is essential to existence, much like air, water, and food, as we spend nearly one-third of our time sleeping. Poor sleep quality or disturbed sleep causes daytime solemnity, which worsens daytime activities' mental and physical qualities and raises the risk of accidents. With advancements in sensor and communication technology, sleep monitoring is moving out of specialized clinics and into our everyday homes. It is possible to extract data from traditional overnight polysomnographic recordings using more basic tools and straightforward techniques. Ballistocardiogram is an unobtrusive, non-invasive, simple, and low-cost technique for measuring cardiorespiratory parameters. In this work, we present a sensor board interface to facilitate the communication between force sensitive resistor sensor and an embedded system to provide a high-performing prototype with an efficient signal-to-noise ratio. We have utilized a multi-physical-layer approach to locate each layer on top of another, yet supporting a low-cost, compact design with easy deployment under the bed frame.
The importance of sleep in the life of a human being to function in nowadays society is known from a large number of studies. A standard method for sleep analysis is polysomnography (PSG), which uses multiple sensors to measure and analyze multiple signals, allowing precise and detailed sleep analysis. Nonetheless, the cost of using PSG technologies is high in terms of complexity, personnel and time. To overcome these shortcomings, alternative solutions can be used to reduce costs and increase comfort for patients. The objective of this work is to design and develop a prototype of the software component of the sleep analysis system, taking into account the aspects of data flow, data storage and user interface in addition to data processing. The software components implemented and developed in the Morpheus System and described in this article comprise a usable platform capable of assisting custom research implementations in IoT.
Measuring cardiorespiratory parameters in sleep, using non-contact sensors and the Ballistocardiography technique has received much attention due to the low-cost, unobtrusive, and non-invasive method. Designing a user-friendly, simple-to-use, and easy-to-deployment preserving less error-prone remains open and challenging due to the complex morphology of the signal. In this work, using four forcesensitive resistor sensors, we conducted a study by designing four distributions of sensors, in order to simplify the complexity of the system by identifying the region of interest for heartbeat and respiration measurement. The sensors are deployed under the mattress and attached to the bed frame without any interference with the subjects. The four distributions are combined in two linear horizontal, one linear vertical, and one square, covering the influencing region in cardiorespiratory activities. We recruited 4 subjects and acquired data in four regular sleeping positions, each for a duration of 80 seconds. The signal processing was performed using discrete wavelet transform bior 3.9 and smooth level of 4 as well as bandpass filtering. The results indicate that we have achieved the mean absolute error of 2.35 and 4.34 for respiration and heartbeat, respectively. The results recommend the efficiency of a triangleshaped structure of three sensors for measuring heartbeat and respiration parameters in all four regular sleeping positions.
Apnea is a sleep disorder characterized by breathing interruptions during sleep, impacting cardiorespiratory function and overall health. Traditional diagnostic methods, like polysomnography (PSG), are unobtrusive, leading to noninvasive monitoring. This study aims to develop and validate a novel sleep monitoring system using noninvasive sensor technology to estimate cardiorespiratory parameters and detect sleep apnea. We designed a seamless monitoring system integrating noncontact force-sensitive resistor sensors to collect ballistocardiogram signals associated with cardiorespiratory activity. We enhanced the sensor’s sensitivity and reduced the noise by designing a new concept of edge-measuring sensor using a hemisphere dome and mechanical hanger to distribute the force and mechanically amplify the micromovement caused by cardiac and respiration activities. In total, we deployed three edgemeasuring sensors, two deployed under the thoracic and one under the abdominal regions. The system is supported with onboard signal preprocessing in multiple physical layers deployed under the mattress. We collected the data in four sleeping positions from 16 subjects and analyzed them using ensemble empirical mode decomposition (EMD) to avoid frequency mixing. We also developed an adaptive thresholding method to identify sleep apnea. The error was reduced to 3.98 and 1.43 beats/min (BPM) in heart rate (HR) and respiration estimation, respectively. The apnea was detected with an accuracy of 87%. We optimized the system such that only one edge-measuring sensor can measure the cardiorespiratory parameters. Such a reduction in the complexity and simplification of the instruction of use shows excellent potential for in-home and continuous monitoring.
Cardiovascular diseases (CVD) are leading contributors to global mortality, necessitating advanced methods for vital sign monitoring. Heart Rate Variability (HRV) and Respiratory Rate, key indicators of cardiovascular health, are traditionally monitored via Electrocardiogram (ECG). However, ECG's obtrusiveness limits its practicality, prompting the exploration of Ballistocardiography (BCG) as a non-invasive alternative. BCG records the mechanical activity of the body with each heartbeat, offering a contactless method for HRV monitoring. Despite its benefits, BCG signals are susceptible to external interference and present a challenge in accurately detecting J-Peaks. This research uses advanced signal processing and deep learning techniques to overcome these limitations. Our approach integrates accelerometers for long-term BCG data collection during sleep, applying Discrete Wavelet Transforms (DWT) and Ensemble Empirical Mode Decomposition (EEMD) for feature extraction. The Bi-LSTM model, leveraging these features, enhances heartbeat detection, offering improved reliability over traditional methods. The study's findings indicate that the combined use of DWT, EEMD, and Bi-LSTM for J-Peak detection in BCG signals is effective, with potential applications in unobtrusive long-term cardiovascular monitoring. Our results suggest that this methodology could contribute to HRV monitoring, particularly in home settings, enhancing patient comfort and compliance.
With the emergence of new sensor technologies, such as fiber optic sensors (FOSs), compared to traditional mechanical sensors, unobtrusive sleep monitoring has been a research focus for decades. This work aims to provide a guide to current bed-based sensor technologies with diverse applications in various settings. We conducted a retrospective literature review, summarizing the state-of-the-art research over the past decade on non-contact bed-based sensor technology in sleep monitoring. We developed a three-category terminology: unobtrusive sensor technology, application, and subject. A total of 263 unique articles were acquired from three databases and screened for relevance, resulting in 21 papers selected for in-depth analysis. The findings revealed eight types of sensors: six mechanical sensors (pressure, accelerometer, piezoelectric, load cell, electromechanical film (EMFI), and hydraulic) and two FOSs (fiber Bragg grating and microbend FOS) that are integrated with or positioned under the bed at three levels of unobtrusiveness. We identified 15 parameters, with heart rate (HR) (14) and respiratory rate (RR) (13) being the most frequently measured. These parameters are generally categorized into three applications: disease-related diagnosis (18), general sleep analysis (9), and general well-being (11). The results indicated that sleep apnea (5) and insomnia (2) were the most frequently detected sleep disorders. Additionally, 59.1% (13) of the systems were tested in a lab environment, with only one undergoing clinical trials. In summary, there is a clear lack of convincing proof of the systems’ effectiveness in continuous in-home sleep monitoring.
Advanced classifiers and feature reduction for accurate insomnia detection using multimodal dataset
(2024)
Sleep deprivation is a significant contributor to various diseases, leading to poor cognitive function, decreased performance, and heart disorders. Insomnia, the most prevalent sleep disorder, requires more effective diagnosis and screening for proper treatment. Actigraphic data and its combination with physiological sensors like electroencephalogram (EEG), electrocardiogram (ECG), and body temperature have proven significant in predicting insomnia using machine learning methods. Studies focusing solely on actigraphic data achieved an accuracy of 84%, combining it with other wearable devices increased accuracy to 88%, and 2-channel EEG alone yielded an accuracy of 92%, but limits scalability and practicality in real-world settings. Here we show that using the hybrid approach of incorporating both recursive feature elimination (RFE) and principal component analysis (PCA) on sleep and heart data features yields outstanding results, with the multi-layer perception (MLP) achieving an accuracy of 95.83% and an F1 score of 0.93. The top-ranked features are predominantly sleep-related and time-domain RR interval. The dependent variables in our study have been extracted from the self-report Pittsburgh Sleep Quality Index questionnaire responses. Our findings emphasize the importance of tailoring feature sets and employing appropriate reduction techniques for optimal predictive modeling in sleep-related studies. Our results demonstrate that the ensemble classifiers generalize well on the dataset regardless of the feature count, while other algorithms are hindered by the curse of dimensionality.
