Key result
Machine learning models using ECG and PPG signals detected active transcutaneous cervical vagal nerve stimulation with 96% accuracy and a receiver operator characteristics area of 0.96.
Why the study?
Transferring transcutaneous cervical vagal nerve stimulation to home settings poses challenges in assessing therapy response, and accurately detecting whether stimulation has been effectively delivered remotely had never been investigated.
Can machine learning analysis of cardiopulmonary signals accurately detect target engagement of transcutaneous cervical vagal nerve stimulation compared to sham?
Can machine learning analysis of cardiopulmonary signals accurately detect target engagement of transcutaneous cervical vagal nerve stimulation compared to sham?
Commonly used wearable sensing modalities like ECG and PPG can accurately detect target engagement of transcutaneous cervical vagal nerve stimulation, potentially enabling remote monitoring of therapy adherence.
May enable remote adherence monitoring for cervical vagal stimulation; hypothesis-generating before clinical validation.
Transcutaneous cervical vagal nerve stimulation (tcVNS) devices are attractive alternatives to surgical implants, and can be applied for a number of conditions in ambulatory settings, including stress-related neuropsychiatric disorders. Transferring tcVNS technologies to at-home settings brings challenges associated with the assessment of therapy response. The ability to accurately detect whether tcVNS has been effectively delivered in a remote setting such as the home has never been investigated. We designed and conducted a study in which 12 human subjects received active tcVNS and 14 received sham stimulation in tandem with traumatic stress, and measured continuous cardiopulmonary signals including the electrocardiogram (ECG), photoplethysmogram (PPG), seismocardiogram (SCG), and respiratory effort (RSP). We extracted physiological parameters related to autonomic nervous system activity, and created a feature set from these parameters to: 1) detect active (vs. sham) tcVNS stimulation presence with machine learning methods, and 2) determine which sensing modalities and features provide the most salient markers of tcVNS-based changes in physiological signals. Heart rate (ECG), vasomotor activity (PPG), and pulse arrival time (ECG+PPG) provided sufficient information to determine target engagement (compared to sham) in addition to other combinations of sensors. resulting in 96% accuracy, precision, and recall with a receiver operator characteristics area of 0.96. Two commonly utilized sensing modalities (ECG and PPG) that are suitable for home use can provide useful information on therapy response for tcVNS. The methods presented herein could be deployed in wearable devices to quantify adherence for at-home use of tcVNS technologies.
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Gurel et al. (2020) studied Traumatic stress triggers (n=26). Transcutaneous cervical vagal nerve stimulation (tcVNS) vs. Sham stimulation was evaluated on Detection of active (vs. sham) tcVNS stimulation presence (96% accuracy, ROC area 0.96). Machine learning models using ECG and PPG signals detected active transcutaneous cervical vagal nerve stimulation with 96% accuracy and a receiver operator characteristics area of 0.96.
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