SVM-based classification method to identify alcohol consumption using ECG and PPG monitoring

Abstract

Driving under the influence (DUI) of alcohol (“drunk driving”) is dangerous and may cause serious harm to people and damage to property. To address this problem, this study developed a system for identifying excess alcohol consumption. Electrocardiogram (ECG) and photoplethysmography (PPG) sensors and intoxilyzers were used to acquire signals regarding the ECG, PPG, and alcohol consumption levels of participants before and after drinking. The signals were preprocessed, segmented, and subjected to feature extraction using specific algorithms to produce ECG and PPG training and test data. Based on the ECG, PPG, and alcohol consumption data we developed a fast and accurate identification scheme using the support vector machine (SVM) algorithm for identifying alcohol consumption. Optimized SVM classifiers were trained using the training data, and the test data were applied to verify the identification performance of the trained SVMs. The identification performance of the proposed classifiers achieved 95% on average. In this study, different feature combinations were tested to select the optimum technological configuration. Because the PPG and ECG features produce identical classification performance and the PPG features are more convenient to acquire, the technological configuration based on PPG is definitely preferable for developing smart and wearable devices for the identification of DUI.