Non-obtrusive system for overnight respiration and heartbeat tracking

Gaiduk, Maksym; Wehrle, Dennis; Seepold, Ralf; Ortega, Juan Antonio

doi:10.1016/j.procs.2020.09.282

Cited by 12 publications

(9 citation statements)

References 17 publications

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“…The main novel point proposed in this study's approach is that it uses only the signals that can be obtained in a non-obtrusive way: movement and breathing [19,20], for which a unique set of parameters was designed. For that purpose, comprehensive literature research and statistical analysis were performed.…”

Section: Discussionmentioning

confidence: 99%

“…However, it would not be possible to perform the measurement described in [39] in a non-obtrusive way because the ECG signal is being measured directly by placing electrodes on the human body. Therefore, our method has a significant advantage as the necessary signals may be measured in a contactless way as described in [19,20], while reporting only a slight reduction of accuracy. Another crucial advantage of our approach is using only four features, compared to 80 in [39], which means faster processing and less computing power needed, which is essential for the embedded systems.…”

Section: Discussionmentioning

confidence: 99%

“…The proposed software system is designed to identify sleep stages out of breathing and movement signals. For that, a set of sensors measuring both these signals (e.g., as described in [19,20]) can be used to quantify a subject's biosignals. These sensors generate numerically quantifiable electrical impulses, the sample values, which change at each of the sleep stages.…”

Section: A Statistical Methodsmentioning

confidence: 99%

“…Other sensors that can record respiratory signals, including changes in their amplitude, can also be applied [e.g. 19,20]. Body movement signals can be replaced by a signal monitored by a 3D acceleration sensor in a recording device placed on the subject's chest.…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Estimation of Sleep Stages Analyzing Respiratory and Movement Signals

Gaiduk

Perea

Seepold

et al. 2022

IEEE J. Biomed. Health Inform.

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The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a nonobtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: A Statistical Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of Sleep Stages Analyzing Respiratory and Movement Signals

Gaiduk

Perea

Seepold

et al. 2022

IEEE J. Biomed. Health Inform.

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“…Is there any complementary approach for cardiorespiratory monitoring using other non-invasive and non-contact technologies when achieving similar performance in relation to a resistive pressure transducer system [28,29]?…”

mentioning

confidence: 99%

Monitoring of Cardiorespiratory Parameters during Sleep Using a Special Holder for the Accelerometer Sensor

Boiko

Gaiduk

Scherz

et al. 2023

Sensors

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Sleep is extremely important for physical and mental health. Although polysomnography is an established approach in sleep analysis, it is quite intrusive and expensive. Consequently, developing a non-invasive and non-intrusive home sleep monitoring system with minimal influence on patients, that can reliably and accurately measure cardiorespiratory parameters, is of great interest. The aim of this study is to validate a non-invasive and unobtrusive cardiorespiratory parameter monitoring system based on an accelerometer sensor. This system includes a special holder to install the system under the bed mattress. The additional aim is to determine the optimum relative system position (in relation to the subject) at which the most accurate and precise values of measured parameters could be achieved. The data were collected from 23 subjects (13 males and 10 females). The obtained ballistocardiogram signal was sequentially processed using a sixth-order Butterworth bandpass filter and a moving average filter. As a result, an average error (compared to reference values) of 2.24 beats per minute for heart rate and 1.52 breaths per minute for respiratory rate was achieved, regardless of the subject’s sleep position. For males and females, the errors were 2.28 bpm and 2.19 bpm for heart rate and 1.41 rpm and 1.30 rpm for respiratory rate. We determined that placing the sensor and system at chest level is the preferred configuration for cardiorespiratory measurement. Further studies of the system’s performance in larger groups of subjects are required, despite the promising results of the current tests in healthy subjects.

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