Breathing detection from tracheal sounds in both temporal and frequency domains in the context of phrenic nerve stimulation

Abstract

Electrical stimulation of the phrenic nerves via implanted devices allows to counteract some disadvantages of mechanical ventilation in patients with high tetraplegia or Ondine's syndrome. Existing devices do not allow to monitor breathing or to adapt the electroventilation to patients' actual needs. A reliable breathing monitor with an inbuilt alarm function would improve patient safety. In our study, a realtime acoustic breathing detection method is proposed as a possible solution to improve implanted phrenic stimulation. A new algorithm to process tracheal sounds has been developed. It combines breathing detection in both temporal and frequency domains. The algorithm has been applied on recordings from 18 healthy participants. The obtained average sensitivity, specificity and accuracy of the detection are: 99.31%, 96.84% and 98.02%, respectively. These preliminary results show a first positive proof of the interest of such an approach. Additional experiments are needed, including longer recordings from individuals with tetraplegia or Ondine Syndrome in various environments to go further in the validation.

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

“…It is noninvasive, safe, easy to perform, low cost and commonly used by a physician to diagnose various cardiopulmonary diseases [56]. The lung sounds obtained by auscultation enables assessment of the airflow through the trachea and bronchial tubes, and it is able to distinguish normal breath sound from abnormal ones, thus aiding diagnosis of pulmonary disorders or evaluation of ventilation [57]. Traditionally, auscultation is performed with a stethoscope, which consists of a small disc-shaped resonator and two tubes connected to earpieces.…”

Respiratory Monitoring Based on Tracheal Sounds: Continuous Time-Frequency Processing of the Phonospirogram Combined with Phonocardiogram-Derived Respiration

“…In our previous studies, we developed and showed the feasibility of one first respiration detection algorithm tested on healthy subjects [ 21 ] and on one patient under implanted phrenic nerve stimulation [ 22 ]. This first algorithm was based on patient-specific thresholds and tested with short recordings.…”

Adaptive Blowing Interaction Method Based on a Siamese Network

“…Maksym presented an algorithm (multinomial logistic regression techniques) for the nonobtrusive recognition of sleep/wake states using signals derived from ECGs, respirations, and body movements captured while lying in a bed [12]. Xinyue Lu presented a new algorithm to process tracheal sounds has been developed that combines breathing detection in both temporal and frequency domains [23]. Mera described a new approach using a noncontact capturing method of breathing activities using a Kinect depth sensor [9].…”