Discrete Wavelet Transforms-Based Analysis of Accelerometer Signals for Continuous Human Cardiac Monitoring

Abstract

Measuring cardiac activity from the chest using an accelerometer is commonly referred to as seismocardiography. Unfortunately, it cannot provide clinically valid data because it is easily corrupted by motion artefacts. This paper proposes two methods to improve peak detection from noisy seismocardiography data. They rely on discrete wavelet transform analysis using either biorthogonal 3.9 or reverse biorthogonal 3.9. The first method involves slicing chest vibrations for each cardiac activity, and then detecting the peak location, whereas the other method aims at detecting the peak directly from chest vibrations without segmentation. Performance evaluations were conducted on signals recorded from small children and adults based on missing and additional peaks. Both algorithms showed a low error rate (15.4% and 2.1% for children/infants and adults, respectively) for signals obtained in resting state. The average error for sitting and breathing tasks (adults only) was 14.4%. In summary, the first algorithm proved more promising for further exploration.

Comparison between Chest-Worn Accelerometer and Gyroscope Performance for Heart Rate and Respiratory Rate Monitoring

“…Afterward, the signal was reconstructed by using the inverse continuous wavelet transform (icwt) between 10 Hz and 40 Hz in order to eliminate frequencies associated with slow-varying trends (e.g., respiratory activity) and isolate only the high-frequency packets representative of cardiac activity. The choice to use icwt was made because it allows for greater selectivity in the frequency band of interest, compared with classical bandpass filters, allowing noise cancellation to be performed without distortion of the raw signal [ 23 , 31 , 32 , 33 ]. Then, the root mean square envelope with a sliding window of 40 samples was applied to the reconstructed signal to emphasize each heartbeat.…”

ECG-Free Heartbeat Detection in Seismocardiography Signals via Template Matching

“…However, research is currently proceeding towards the development of automated, standalone (i.e., ECG-independent) methods, most of which rely on SCG signals. SCG-based heartbeat detectors exploit different approaches, such as signal envelope extraction, often combined with a thresholding operation [ 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 ]; continuous or discrete wavelet transform [ 70 , 71 ]; variational mode decomposition [ 72 ]; autocorrelated differential algorithm [ 58 ]; matched filtering [ 73 ]; probabilistic methods, e.g., the hidden Markov model [ 74 ]; machine learning [ 75 ]; and deep learning [ 76 , 77 ]. Of these, the heartbeat detection methods reported in [ 58 , 63 ] were performed on SCG acquisitions combined with simultaneous GCG recordings.…”

Special Issue: “Research on Biomedical Signal Processing”

“…Ferdinando et al, in their paper [3], proposed continuous human cardiac monitoring by analyzing signals from accelerometers placed on the chest.…”