Abstract: The invention relates to a method for detecting a change in a patient's physiological condition with respect to a reference physiological condition. The method is based on a multimodal analysis that involves measurements of electroencephalography signals Si from the patient and at least one other physiological signal SN+1(N?1) from the patient. Based on these measurements, the invention proposes calculating distances di(m) associated with the electroencephalography measurements and with the measurements of the other physiological signals and fusing these data, the data having previously undergone a certain number of mathematical processing operations. In the context of the invention, the fused data may be assigned a weighting coefficient chosen according to one or more a priori or a posteriori criteria.

Abstract: A method for monitoring physiological signals of a subject from sounds produced by the subject, including: receiving recorded sounds, including sounds from the subject's chest and being transmitted by the subject's biological tissues to the subject's ears, the recorded sounds being recorded by sound recording element(s) positioned inside earcup(s) of headphones worn by the subject; receiving signals from an accelerometer and a gyroscope being recorded simultaneously with the recorded sounds; detecting heart beats from the cardiac peaks sounds and calculating inter-beat intervals from the heart beats; extracting a first estimation of the breathing signal from the inter-beat intervals presenting respiratory sinus arrhythmia; extracting a second estimation of the breathing signal from residual sounds; extracting a third estimation of the breathing signal and motion artifacts from the signals of the accelerometer and the gyroscope; calculating the breathing signal by combining the first, second and third estimati

Abstract: A method for assessing an electroencephalographic signal quality based on a multiclass classification, including: receiving at least one segment of electroencephalographic signal from at least one electrode; extracting at least one feature value from each electroencephalographic signal segment channel; classifying with a first classification to assign each electroencephalographic signal segment channel to one of at least three quality classes.