US 12,220,241 B2
Electronic device and method for selecting feature of electrocardiogram
Jun-Hong Chen, New Taipei (TW); Chun-Hsien Li, New Taipei (TW); Yun-Hsuan Chan, New Taipei (TW); Ting-Fen Tsai, Miaoli County (TW); and Chi-Hsiao Yeh, Keelung (TW)
Assigned to National Health Research Institutes, Miaoli County (TW); Chang Gung Memorial Hospital, Keelung, Keelung (TW); Acer Medical Inc., New Taipei (TW); and Acer Incorporated, New Taipei (TW)
Filed by National Health Research Institutes, Miaoli County (TW); Chang Gung Memorial Hospital, Keelung, Keelung (TW); Acer Medical Inc., New Taipei (TW); and Acer Incorporated, New Taipei (TW)
Filed on Apr. 19, 2021, as Appl. No. 17/233,573.
Claims priority of application No. 109146617 (TW), filed on Dec. 29, 2020.
Prior Publication US 2022/0202343 A1, Jun. 30, 2022
Int. Cl. A61B 5/352 (2021.01); A61B 5/353 (2021.01); A61B 5/36 (2021.01); G06N 20/00 (2019.01); G16H 30/20 (2018.01)
CPC A61B 5/352 (2021.01) [A61B 5/353 (2021.01); A61B 5/36 (2021.01); G06N 20/00 (2019.01); G16H 30/20 (2018.01)] 12 Claims
OG exemplary drawing
 
1. An electronic device for selecting a feature of an electrocardiogram, comprising:
a transceiver;
a storage medium, storing a plurality of modules; and
a processor, coupled to the storage medium and the transceiver, and accessing and executing the plurality of modules, wherein the plurality of modules comprise:
a data collection module, obtaining the electrocardiogram through the transceiver;
a first pre-processing module, performing a first pre-processing on the electrocardiogram to generate a first electrocardiogram;
a second pre-processing module, performing a second pre-processing on the first electrocardiogram to generate a second electrocardiogram;
a marking module, marking a plurality of extreme points corresponding to at least one type of wave comprising an R wave on the first electrocardiogram by: dividing the second electrocardiogram into a plurality of portions according to a window function; marking a data point with a maximum value in a first portion in the plurality of portions as a first reference point in response to the first portion comprising at least one data point greater than zero, wherein the first reference point corresponds to a first reference time point; determining a first period of the first electrocardiogram according to the first reference time point; and marking a second data point with a maximum value in the first period as a first R wave extreme point, wherein the first reference time point is located at a center of the first period, wherein the plurality of extreme points comprise the first R wave extreme point;
a feature establishing module, calculating a first feature value corresponding to a first feature according to the plurality of extreme points of the at least one type of wave, and generating a first performance index corresponding to a machine learning model according to the first feature value;
a feature selection module, determining whether to select the first feature according to the first performance index; and
an output module, outputting the first feature through the transceiver in response to selecting the first feature.