Non-Contact Heartbeat Detection by Heartbeat Signal Reconstruction Based on Spectrogram Analysis With Convolutional LSTM

Abstract

Heartbeat detection is one of key techniques to monitor our health condition in daily life, and demands for this technique have increased year and year. Thanks to the non-contact and non-invasive features, various Doppler sensor-based detection methods have been investigated so far. However, the heartbeat detection accuracy of the conventional methods could get degraded due to the low SNR (Signal-to-Noise Ratio) of heartbeat components. Thus, even after some signal processing, non-heartbeat components still remain over such processed signal, which could degrade the heartbeat detection accuracy. In particular for the subjects with low HR (Heart Rate), the estimated HR tends to be higher than the ground truth HR due to such non-heartbeat components, though the conventional methods have mainly focused on the heartbeat detection against the subjects with the normal HR higher than 50 bpm (Beats Per Minute). In this paper, to accurately detect heartbeat even with low HR via a Doppler sensor, we propose a heartbeat detection method based on heartbeat signal reconstruction with convolutional LSTM (Bidirectional-Long Short-Term Memory). In the proposed method, to reconstruct a heartbeat signal based on the periodicity of heartbeat and the spectrum distribution peculiar to heartbeat, successive spectrograms that might be due to heartbeat is used as an input to convolutional LSTM. In addition, for better reconstruction of a heartbeat signal, the previously estimated RRI (R-R Interval) is also used as a feature in the proposed deep learning model with convolutional LSTM. Through the experiments, we confirmed that our proposed method accurately detected heartbeat against 17 subjects including the ones with the HR lower than 50 bpm.

ECG Signal Reconstruction Using FMCW Radar and a Convolutional Neural Network for Contactless Vital-Sign Sensing

“…In contrast to waveform-reconstruction methods for ECG signals, most methods for detecting heartbeats by using radar focus on R-peaks. One such method [7] uses convolutional long short-term memory (LSTM) to construct a model that takes a time-frequency representation of radar signal as input, gives a band-pass filtered ECG signal as an output, and calculates RRI by peak detection. Another method [6] creates two triangular waves whose maxima match R and Speaks, and triangular waves are estimated from the radar signals by using a CNN and a recurrent neural network (RNN) combined model.…”

Elicitation of Anxiety Without Time Pressure and Its Detection Using Physiological Signals and Artificial Intelligence: A Proof of Concept

“…Regarding HR, features were extracted from frequencies between 0 and 0.2 Hz, widely used to assess heart rate variations related to respiration [41]. Features were also extracted from 0.5 to 2 Hz because the heartbeat range is typically between 30 bpm (i.e., 0.5 Hz) and 120 bpm (i.e., 2 Hz) [42]. Finally, as highlighted in [43], [44], the frequency band that showed the highest PPG power density distribution was [0.5, 8] Hz: PPG features were thus extracted from this band.…”

Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validation

“…Heart rate, as the four vital signs of human body, is a basic indicator to measure a person's health status [ 1 , 2 ]. Heartbeat detection and analysis can effectively evaluate sympathetic and parasympathetic nerve activity state of subjects, so as to deduce the emotional change, stress level, sleep quality, and other physical and psychological condition information [ 3 ]. This has positive implications for the health and care of the subjects.…”