Compression of PPG Signal Through Joint Technique of Auto-Encoder and Feature Selection

Abstract

PPG signal utilize the light-based method to sense the blood-flow-rate as controlled by the actions of heart’s pumping. It is extensively utilized in the healthcare with application ranging from the pulse oximetry in the serious care units to the heart rate (HR) measurement in the wearable devices. This paper introduces the algorithm known as PPGC-AE-FS (PPG-Signal Compression using Auto-Encoder and Feature Selection) that is the combined generative method, which incorporates FS and AE together. At the end, our introduced algorithm can differentiate the task as relevant units through not relevant task to get very effective feature for the classification task. Our method not only accomplishes the FS on the learned level of higher feature, but also endows the AE to construct the discriminative units. Our experimental outcomes on many benchmarks that demonstrate our model is much better than existing methods.