Forecasting of Breathing Events from Speech for Respiratory Support

When a patient using a breathing support system such as a portable oxygen concentrator (POC) talks, the flow of oxygen to the lungs is disturbed. The ideal moment to administer oxygen-rich air during talking would be the brief inhale moments between utterances. However, the detection of the inhale moment is difficult and the latency of the air transfer from the pump, through the hose, to the nasal cannula may be larger than the inspiratory period in normal speech. The prediction of the next inhale moment could be used to compensate the latency and therefore provide a significantly better support for a patient who needs oxygen-rich air but want to communicate normally. In this paper we provide the first evidence that it is possible to forecasts the next inhale moment from the speech of the talker using deep learning techniques. Breathing forecasting from speech has also other potential applications that we briefly discuss in the paper.