Abstract: A method to detect drowsiness of an individual. The method includes: (a) acquiring a cardiac signal; (b) processing the cardiac signal to detect time intervals between successive heartbeats; (c) extracting, from the time intervals, characteristic HRV variable(s) of the heart rate variability, each HRV variable being obtained from a plurality of the time intervals; (d) calculating at least one direction aggregate for the HRV variable(s), each being a variable calculated from the HRV variable in a sliding time window, and which characterizes a trend in the window, and/or calculating at least one shape aggregate for the HRV variable(s), each being calculated from values of the HRV variable in a sliding time window and quantifying the shape of a distribution of values of the HRV variable in the window; and (e) processing the direction aggregate(s) and/or the shape aggregate(s) using a detection algorithm enabling detection of drowsiness of the individual.

Abstract: The method allows controlling the quality of an initial RR series made up of a plurality of (RRi) samples which are respectively a function of time intervals (?ti) which separate two successive heartbeats. During this method, one resamples the RR series so as to obtain a resampled RR series, and one automatically controls the quality of the RR series by automatically calculating at least the mathematical norm value (NORME), in a sliding window, of the resampled RR series, said mathematical norm value being given by the following formula: NORME = ? i = 1 N ? ( RR i - 1 N ? ? i = 1 N ? ( RR i ) ) 2 where N is the number of RRi samples in said window.

Abstract: The method allows controlling the quality of an initial RR series made up of a plurality of (RRi) samples which are respectively a function of time intervals (?ti) which separate two successive heartbeats. During this method, one resamples the RR series so as to obtain a resampled RR series, and one automatically controls the quality of the RR series by automatically calculating at least the mathematical norm value (NORME), in a sliding window, of the resampled RR series, said mathematical norm value being given by the following formula: NORME = ? i = 1 N ? ( RR i - 1 N ? ? i = 1 N ? ( RR i ) ) 2 where N is the number of RRi samples in said window.

Abstract: The method allows controlling the quality of an initial RR series consisting of a plurality of (RRi) samples which are respectively a function of time intervals (?ti) which separate two successive heartbeats. During this method, one resamples the RR series so as to obtain a resampled RR series, and one automatically controls the quality of the RR series by automatically calculating at least the mathematical norm value (NORME), in a sliding window, of the resampled RR series, said mathematical norm value being given by the following formula: NORME = ? i = 1 N ? ( RR i - 1 N ? ? i = 1 N ? ( RR i ) ) 2 where N is the number of RRi samples in said window.

Abstract: The method is used for filtering an initial RR series of a plurality of (RRi) samples which are respectively a function of the time intervals (?ti) that separate two successive heartbeats. In order to carry out this filtering, one must automatically detect in the initial RR series if one or more successive (RRi) samples are incorrect, and automatically correct in the RR series one or more of the (RRi) samples detected as being incorrect by replacing them with one or more reconstructed (RRc) samples so as to obtain an RR series. One must automatically control the quality of the RR series by counting, in a predefined sliding window, the number (NbPertub) of (RRc) samples of the RR series that were reconstructed, and/or, if applicable, the number (NbPertub) of (RRrc) samples of the RR series that were reconstructed and resampled.

Abstract: The method allows controlling the quality of an initial RR series consisting of a plurality of (RRi) samples which are respectively a function of time intervals (?ti) which separate two successive heartbeats. During this method, one resamples the RR series so as to obtain a resampled RR series, and one automatically controls the quality of the RR series by automatically calculating at least the mathematical norm value (NORME), in a sliding window, of the resampled RR series, said mathematical norm value being given by the following formula: NORME = ? i = 1 N ? ( RR i - 1 N ? ? i = 1 N ? ( RR i ) ) 2 where N is the number of RRi samples in said window.

Abstract: The inventive method for processing an RR series comprises a plurality of (RR) samples representing time intervals (dti) between two successive heart beats or the inverse (1/dti) of said time intervals consists in selecting (N) (RRi) samples in a main time window having a predetermined length (n), in cutting said main window into (m) subwindows (Fj), in calculating an intermediate parameter (A1) for each subwindow (Fj) on the basis of the (RRi) samples contained in the subwindow (Fj) and in calculating a final parameter as a function of the intermediate parameters (Aj). The invention is used for analysing a cardiac rhythm variability, in particular for assessing a patient's pain or stress.

Abstract: The inventive method for processing an RR series comprises a plurality of (RR) samples representing time intervals (dti) between two successive heart beats or the inverse (1/dti) of said time intervals consists in selecting (N) (RRi) samples in a main time window having a predetermined length (n), in cutting said main window into (m) subwindows (Fj), in calculating an intermediate parameter (A1) for each subwindow (Fj) on the basis of the (RRi) samples contained in the subwindow (Fj) and in calculating a final parameter as a function of the intermediate parameters (Aj).The invention is used for analysing a cardiac rhythm variability, in particular for assessing a patient's pain or stress.