The Application of Deep Learning Algorithms for PPG Signal Processing and Classification

Abstract

Photoplethysmography (PPG) is widely used in wearable devices due to its conveniency and cost-effective nature. From this signal, several biomarkers can be collected, such as heart and respiration rate. For the usual acquisition scenarios, PPG is an artefact-ridden signal, which mandates the need for the designated classification algorithms to be able to reduce the noise component effect on the classification. Within the selected classification algorithm, the hyperparameters’ adjustment is of utmost importance. This study aimed to develop a deep learning model for robust PPG wave detection, which includes finding each beat’s temporal limits, from which the peak can be determined. A study database consisting of 1100 records was created from experimental PPG measurements performed in 47 participants. Different deep learning models were implemented to classify the PPG: Long Short-Term Memory (LSTM), Bidirectional LSTM, and Convolutional Neural Network (CNN). The Bidirectional LSTM and the CNN-LSTM were investigated, using the PPG Synchrosqueezed Fourier Transform (SSFT) as the models’ input. Accuracy, precision, recall, and F1-score were evaluated for all models. The CNN-LSTM algorithm, with an SSFT input, was the best performing model with accuracy, precision, and recall of 0.894, 0.923, and 0.914, respectively. This model has shown to be competent in PPG detection and delineation tasks, under noise-corrupted signals, which justifies the use of this innovative approach.

Physiological sensor data cleaning with autoencoders

“…Some other approaches include waveform morphology analysis combined with a decision-tree classifier (Sukor et al 2011), developing an artifact detector on the PPG signal (Robles-Rubio et al 2013), or constructing a PPG signal quality index based on features derived by the signal (Orphanidou et al 2014, Zanon et al 2020. Esgalhado et al (2021) proposed a CNN-LSTM classifier for PPG signal quality classification and reported accuracy of 89.4% (i.e. 3% lower than our best reported accuracy).…”

A learning-based image processing approach for pulse wave velocity estimation using spectrogram from peripheral pulse wave signals: An in silico study

“…Regarding the window type, it has been shown that Hamming and Kaiser's windows are good options for generating spectrograms from pulse wave signals such as PPG (Zong and Jafari, 2015;Esgalhado et al, 2021). Overlapping percentage values of 0, 60, and 95 for Hamming windows and 0, 61, and 70 for Kaiser, were selected based on the values reported in (Trethewey, 2000) and (Heinzel et al, 2002).…”

Photoplethysmography-Based Distance Estimation for True Wireless Stereo

“…PPG sensors are widely employed in wearable devices [ 5 , 6 ]. PPG sensors emit infrared rays to the skin and measure the amount of blood flow by determining the amount of rays absorbed in red blood cells.…”

“…The DeepCNAP model for heart rate measurement using PPG signals was presented [ 14 ]. A deep learning model for robust PPG wave detection was proposed in [ 5 ]. The best performing model was a CNN-long short-term memory (LSTM) algorithm with a PPG synchro-squeezed Fourier transform (SSFT) and the accuracy, precision, and recall were 0.894, 0.923, and 0.914, respectively.…”