An Efficient Heart Rate Measurement System Using Medical Radar and LSTM Neural Network

Abstract

This research proposes a noncontact heart rate measurement method using medical radar and artificial intelligence techniques. This technique has a significant role in the design and development of a wireless system that monitors the body’s vital signs. Firstly, based on a signal model describing chest surface movement, we propose a method to create a dataset for the training process using the long-short-term memory model. Secondly, a novel method to extract chest motion from the received radar signal is proposed. Finally, the heart rate will be estimated by using the obtained model and the received motion signal. The performance of the proposed method is evaluated through the root mean square error parameter as well as compared with other methods. Experimental results evaluated according to Bland–Altman achieved an accuracy of 96.67%.

Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

“…Continuous vital signs monitoring can prevent life-threatening scenarios by providing early warnings before a decline of the patient's status [104]. In this sub-category of papers on value estimation we include papers dealing with the estimation of vital signs, such as HR (n = 21) [17], [22], [25], [28], [29], [52], [55]- [57], [62], [64], [74], [76], [82], [87], [91], [93], [96]- [98], BR [58], [66], [88], [89], [99], [102] (n = 6), both HR and BR [31], [34], [40], [53], [54], [92], [103] (n = 7), BP [65], [67], [75], [83], [85], [100], [101] (n = 7), oxygen saturation (SpO2) [84] (n = 1), and spyrometric indices [61] (n = 1) in a given windows of observation.…”

“…For the hybrid architecture the deep learning model used was an encoder-decoder type in combination with extracted features from K-nearest neighbour. The general [17], [22], [25], [28], [29], [31], [34], [40], [52]- [58], [61], [62], [64]- [67], [74]- [76], [82]- [89], [91]- [93], [96]- [103] Signal reconstruction (n = 18)…”

“…value estimation (n=6) [74], [76], [83], [85], [93], [99]; signal reconstruction (n=3) [36], [41], [69] MRE= N i (predictioni − truevaluei) value estimation (n=5) [61], [65], [83], [97], [110]; signal reconstruction (n=3) [30], [36], [44], [94]; physiological event detection (n=1…”

“…The filtering row lists the filtering procedures adopted to remove the clutter and noise in the maps which can be further processed by locating the subject in the target location row. Filtering the radar data or locating the subject is a step that appears in 57% of the included studies; typical filters are band pass filters (often Butterworth) in the adequate cutoff frequencies for the wanted vital sign (n = 15) [28], [29], [40], [44], [58], [72], [74]- [76], [79], [81], [91], [93], [95], [99], [103] and derivative filters (n = 4) [36], [44], [81], [91]; typical algorithm for target location are threshold based detection (n = 13) [29], [30], [34], [41], [44], [55], [57], [63], [66], [68], [69], [94], [96]. The augmentation row shows the techniques to increase the dataset size in a synthetic way.…”

Ultra-Wideband Radar for Simultaneous and Unobtrusive Monitoring of Respiratory and Heart Rates in Early Childhood: A Deep Transfer Learning Approach

“…In [ 26 ], a long short-term memory network (LSTM) approach is proposed to extract HR for adults, which works well based on a motion and distortion correction method named as Eclipse Fit method. However, this type of motion correction performance degrades in high levels of noise (which could possibly be the case for hospital and home environments and the various uncontrolled clutters present there).…”