Sebastijan Sprager

Heartbeat and Respiration Detection From Optical Interferometric Signals by Using a Multimethod Approach

In this paper, a multimethod approach for heartbeat and respiration detection from an optical interferometric signal is proposed. Optical interferometer is a sensitive device that detects physical changes of optical-fiber length due to external perturbations. When in direct or indirect contact with human body (e.g., hidden in a bed mattress), mechanical and acoustic activity of cardiac muscle and respiration reflect in the interferometric signal, enabling entirely unobtrusive monitoring of heartbeat and respiration. A novel, two-phased multimethod approach was developed for this purpose. The first phase selects best performing combinations of detection methods on a training set of signals. The second phase applies the selected methods to test set of signals and fuses all the detections of vital signs. The test set consisted of 14 subjects cycling an ergometer until reaching their submaximal heart rate. The following resting periods were analyzed showing high efficiency (98.18 ± 1.40% sensitivity and 97.04 ± 4.95% precision) and accuracy (mean absolute error of beat-to-beat intervals 22±9 ms) for heartbeat detection, and acceptable efficiency (90.06 ± 7.49% sensitivity and 94.21 ± 3.70% precision) and accuracy (mean absolute error of intervals between respiration events 0.33 ± 0.14 s) for respiration detection.

Detection of heartbeat and respiration from optical interferometric signal by using wavelet transform

A novel approach for the heartbeat and respiration detection based on optical interferometer and wavelet transform is proposed in this paper. Optical interferometer is a sensitive device that detects physical elongation of optical fibre due to external perturbations. Mechanical activity of cardiac muscle and respiration reflects in interferometric signal when the interferometer is in contact with human body and, thus, enables unobtrusive detection of human vital signs. The efficiency and accuracy of the proposed approach was estimated in two experimental protocols. The first one collected interferometric signals from 20 subjects during rest. In the second experiment, 10 participants cycled an ergometer until reaching their submaximal heart rate, and were measured immediately after that. Heartbeat detection results show high efficiency (99.46±1.11% sensitivity, 99.60±1.05% precision) and accuracy (mean relative error (MRE) of beat-to-beat intervals 3.16±2.32%) for the first experiment, and slightly lower efficiency (96.22±2.96% sensitivity, 95.35±3.03% precision) and accuracy (MRE of 9.56±3.67%) for the second experiment. Considering respiration detection, high efficiency (97.64±7.28% sensitivity, 99.38±2.80% precision) and accuracy (MRE of intervals between respiration events 7.37±7.20%) for the first experiment, and acceptable efficiency (92.05±6.10% sensitivity, 93.45±3.08% precision) and accuracy (MRE of 16.28±6.25%) for the second experiment confirm a practical value of proposed approaches.

Monitoring of basic human vital functions using optical interferometer

In this paper, a method is presented for monitoring of basic human vital functions (heartbeat and respiration) by using optical fibre interferometer. Optical fibre interferometer is a sensitive device which detects physical changes of the optical fibre length. If a person is in physical contact with the fibre, such as having it in bed, the fibre interferometers responds to small changes in fibre length induced by the heartbeat and breathing. This allows for unobtrusive and continuous monitoring of a persons vital life signs. The interferometer, however, has a cosine signal transfer characteristic, which complicates the feature extraction algorithms. An experiment was performed with the optical fibre on the mattress. Heartbeat and respiration were extracted using a method based on zero-crossings in the measured interferential signal and a preselected filter bank. The methods recognition rate and accuracy were verified by using reference sensors (an ECG apparatus and nasal air-flow measurements). All signals were acquired simultaneously from 5 male persons in duration of about 60 s. The obtained results prove this simple, unobtrusive approach can monitor human vital signs with fairly high recognition rate: about 95.7% for heartbeat and 93.8% for respiration.

Impact of different walking surfaces on gait identification based on higher-order statistics of accelerometer data

This paper presents an investigation on how different walking surfaces affect gait identification based on accelerometer data. Accelerometer data of 5 subjects walking on 4 different solid surfaces was acquired on 3 different days by a cell phone placed on the subjects hip. Data analysis detects gait cycles by a method based on wavelet transform. For all gait cycles, features were calculated by using higher-order statistics. Similarity estimation for discerning the different subjects and surfaces was introduced by using principal component analysis (PCA). It proved that the different solid surfaces do not influence the efficiency of the identification of subjects based on their gait.

Optimization of heartbeat detection in fiber-optic unobtrusive measurements by using maximum a posteriori probability estimation.

This paper deals with an optimized heartbeat detection multimethod by using maximum a posteriori probability (MAP). The approach was derived for unobtrusive fiber-optic measurements of cardiac activity. Multiple independent detection methods were selected and characterized by their delays and variability referring to cardiac electrical excitation (R waves). Validation of the approach was performed in two experiments involving 24 participants: 10 interferometric signals were recorded at rest, 14 with variable heart rate after physical exercise. The proposed MAP heartbeat detection was assessed by a cross validation in 250 iterations. Obtained results show the overall efficiency, which was estimated by a product of the sensitivity, precision, and variability of heartbeat detections, yields 97.04 ± 3.36% for the experiment with physical exercise and 97.07 ± 4.49% at rest. The methods accuracy guarantees that the heartbeat detections differ for 22 ± 5 ms and 22 ± 3 ms from the ECG reference in the two types of experiments, respectively.

Optimization of heartbeat detection based on clustering and multimethod approach

In this paper, an approach for optimizing heartbeat locations as detected in time by multimethod approach is proposed. The approach builds a two-dimensional representation of heartbeat locations obtained by several independent detection methods. The representation depends on heartbeat time instants and beat-to-beat intervals. It is first transformed into a smoothed two-dimensional histogram of points indicating individual heartbeat detections. Heartbeat time instants are determined as local maxima in the histogram. We tested our approach on signals acquired by optical interferometer. Seven subjects participated in the experiment, beginning by an ergometer exercise until they reached submaximal heart rate. A resting period followed, during which optical interferometric signal was taken unobtrusively in parallel with referential ECG. The proposed detection procedure was capable of tracking the changing heart rhythm by analyzing optical interferometric signals and comparing the results to the referential ECG recordings. Sensitivity 97.13±2.00% and precision 97.82±2.09% were obtained. Mean absolute error between detected beat-to-beat and referential RR intervals yielded 20.05±8.38 ms and corresponding mean relative error 7.47±3.19%.

Detection of the first heart sound using fibre-optic interferometric measurements and neural networks

Fiber-optic interferometry is used to measure subtle changes of the optical fibre length. It has been shown that in this way also the heart activity can be detected if the fibre is in direct or indirect contact with human body. The measured interferometric signal must be first demodulated and band-pass filtered to separate superimposed contributions of signal components. Only then their detection and classification is feasible. In this paper, we deploy feedforward neural network for detecting the first heart sound (S1) from fibre-optic interferometric signals. A reliable and robust classification of S1 and finding its location in time importantly support diagnosing of cardiac arrhythmias and valve abnormalities. Our experimental results on a group of ten healthy subjects that underwent submaximal stress testing before fibre-optic measurements yield 98.2±1.5% and 98.4±0.9% for sensitivity and precision of S1 detection, respectively.

Overnight heartbeat monitoring by using unobtrusive optical interferometric measurements

In this paper, a possibility of overnight monitoring by using optical interferometric signals was examined. Optical interferometer is a sensitive device that detects physical changes of the length of optical fibre. When used as bed sensor, heartbeats cause its perturbation when a subject is lying on the mattress. This allows continuous monitoring of heartbeat entirely unobtrusively. Overnight acquisition was performed on a healthy male subject and lasted 9.75 hours during subjects sleep. The signal was divided into 15-minute segments. Heartbeat detection was applied to each segment individually. Results were validated by using a referential ECG signal. High efficiency was obtained with sensitivity of 97.21±2.11% and precision of 97.76±1.04%, and accuracy of heartbeat detection with overall mean absolute error of 100.84±19.22 ms, overall median absolute error of 75.56±15.03 ms, and overall P90 of 219.69±46.91 ms.

Unobtrusive monitoring of biomedical signals in home environment

This paper describes recent developments in processing of signals acquired by on-body and environmental sensors to extract human vital signs. Crucial benefits of the approaches explained are in unobtrusive sensors and efficient estimation algorithms. Optical fibre interferometer (OFI) is presented in this context in more detail. We derived methods for extracting heartbeat and breathing rates from optical fibre signals. Experiments during rest were conducted with the sensor placed in bed mattress, while on-body placement of OFI was deployed in dynamic condition. Heartbeat and breathing rates were assessed by using time-frequency approaches, and simultaneously verified with referential medical devices. Preliminary results demonstrate high sensitivity and precision of proposed algorithms (over 97% for heartbeat in rest).

Monitoring respiration by using fiber-optic interferomtery and maximum a posteriori estimation

This paper reveals a novel approach to recognize respiration events by using an unobtrusive fiber-optic sensor. When the optical fiber is in a direct or indirect contact with human body, it detects weak contributions of vital signs. After a demodulation of fiber-optic signal, breathing can be sorted out by observing variations of heartbeat amplitudes. Introduced into a maximum a posteriori (MAP) estimation, it can locate time instants of inspirations and expirations. Estimates in experiments conducted after submaximal exercise tests in 14 young healthy subjects yield overall sensitivity 94.4±5.62% and overall precision 96.02±2.26% for inspirations. Overall mean absolute error of the inspiration onsets equals 260±143 ms. Similar figures apply to expirations.