Accelerating Biomedical Signal Processing Using GPU: A Case Study of Snore Sound Feature Extraction.

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Abstract

The advent of ‘Big Data’ and ‘Deep Learning’ offers both, a great challenge and a huge opportunity for personalised health-care. In machine learning-based biomedical data analysis, feature extraction is a key step for ‘feeding’ the subsequent classifiers. With increasing numbers of biomedical data, extracting features from these ‘big’ data is an intensive and time-consuming task. In this case study, we employ a Graphics Processing Unit (GPU) via Python to extract features from a large corpus of snore sound data. Those features can subsequently be imported into many well-known deep learning training frameworks without any format processing. The snore sound data were collected from several hospitals (20 subjects, with 770-990 MB per subject – in total 17.20 GB). Experimental results show that our GPU-based processing significantly speeds up the feature extraction phase, by up to seven times, as compared to the previous CPU system.