Automatic cough detection via a multi-sensor smart garment using machine learning

Coughing behavior is associated with conditions such as sleep apnea, asthma, and chronic obstructive pulmonary disorder and can severely affect quality of life in those affected. In this context, coughing quantification is often important, but routinely performed via questionnaires. This approach is dependent on patient compliance or recall, which may affect validity and be especially difficult for nocturnal coughs. Manual review of audio recordings is potentially more accurate, but raises privacy concerns due to the collection and review of sensitive audio-data by a human annotator. Today, machine learning approaches are increasingly used to quantify coughs; however, algorithms often rely on microphone recordings, resulting in the same privacy issues, especially if data are sent to a remote server for analysis. The aims of this study are to determine if (1) a suite of sensors, excluding microphone recordings, can accurately detect coughs unobtrusively and (2) what the relative importance of each sensor-type on model performance may be. Data from 44 healthy young adult participants performing on-demand coughs and other tasks (breathing, talking, throat clearing, laughing, sniffing) in supine and sitting conditions were collected for this observational, cross-sectional study using a multi-sensor smart-garment device. Synchronized video was used to annotate tasks. Three-dimension acceleration, respiration (inductance plethysmography), and electrical activity (electrocardiography) signals were extracted into 1 s strips and binarized into coughs and non-coughs. Data were split into train and test sets using an inter-subject 80:20 split, ensuring that data from a particular participant are found in a single set. This procedure was repeated 10 times with different random inter-subject splits to assess the variability of results. Statistical and frequency-based features were computed and used as inputs to a Random Forest Classifier to predict classes (cough vs not-cough). Model hyperparameters were tuned to maximize F1-score using five-fold cross validation of the training set. Final model performance was assessed using F1-score, precision, and recall (sensitivity) on the test sets with mean (standard deviation) reported. Single sensor models based on acceleration, respiration, or electrocardiography revealed F1 scores of 92.6 (1.2)%, 88.9 (3.2)%, and 77.5 (3.4)%, respectively. Overall, the dual (acceleration, respiration) sensor model achieved the highest performance (F1-score 93.0 (1.1)%, precision 84.2 (4.2)%, and recall 95.5 (1.6)%). The multi-modal wearable device was able to distinguish coughs from other respiratory maneuvers, with acceleration and respiration sensors providing the most valuable information. Future studies could implement this approach for remote monitoring of coughs in patients suffering from coughing symptoms.