Patient illness classification using time-frequency features derived from the photoplethysmogram

被引：0

作者：

Watson, JN ^{[1
]}

Addison, PS ^{[1
]}

Leonard, P ^{[1
]}

Beattie, TF ^{[1
]}

机构：

[1] Elvingston Sci Ctr, Cardiodigital Ltd, Gladsmuir, East Lothian, Scotland

来源：

PROCEEDINGS OF THE 25TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-4: A NEW BEGINNING FOR HUMAN HEALTH | 2003年 / 25卷

关键词：

photoplethysmogram; wavelet transform; illness identification; illness severity;

D O I：

10.1109/IEMBS.2003.1280544

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

We report on preliminary results from a study in which statistical features derived from the time-frequency decomposition of the plethysmographic waveform were used as input to a classifier of patient illness severity. It is shown how features derived from a wavelet decomposition can be used to identify 'ill' patients from a mixed 'ill' / 'healthy' data set. Further, an attempt to stratify according to patient illness severity is described.

引用

下载

页码：2978 / 2981

页数：4

共 50 条

[1] Time-Frequency and Phase Derived Features for Emotion Classification
Lalitha, S.
Chaitanya, K. K.
Teja, G. V. N.
Varma, K. Vijith
Tripathi, Shikha
2015 ANNUAL IEEE INDIA CONFERENCE (INDICON), 2015,
[2] Classification of radar clutter using features extracted from the time-frequency domain
Jouny, I
Wu, C
AUTOMATIC TARGET RECOGNITION VII, 1997, 3069 : 49 - 60
[3] System classification by using discriminant functions of time-frequency features
Mendoza Reyes, Miguel
Lorenzo-Ginori, Juan V.
Taboada-Crispi, A.
Luna Carvajal, Yakelin
PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS AND APPLICATIONS, PROCEEDINGS, 2006, 4225 : 920 - 928
[4] Time-frequency Features for sEMG Signals Classification
Karheily, Somar
Moukadem, Ali
Courbot, Jean-Baptiste
Abdeslam, Djaffar Ould
PROCEEDINGS OF THE 13TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 4: BIOSIGNALS, 2020, : 244 - 249
[5] Estimation of Respiratory Rate From Photoplethysmogram Data Using Time-Frequency Spectral Estimation
Chon, Ki H.
Dash, Shishir
Ju, Kihwan
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2009, 56 (08) : 2054 - 2063
[6] A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals
Durongbhan, Pholpat
Zhao, Yifan
Chen, Liangyu
Zis, Panagiotis
De Marco, Matteo
Unwin, Zoe C.
Venneri, Annalena
He, Xiongxiong
Li, Sheng
Zhao, Yitian
Blackburn, Daniel J.
Sarrigiannis, Ptolemaios G.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2019, 27 (05) : 826 - 835
[7] sEMG time-frequency features for hand movements classification
Karheily, Somar
Moukadem, Ali
Courbot, Jean-Baptiste
Abdeslam, Djaffar Ould
EXPERT SYSTEMS WITH APPLICATIONS, 2022, 210
[8] Extraction of optimal time-frequency plane features for classification
Kalkan, Habil
Ince, Firat
Tewfik, Ahmed
Yardimci, Yasemin
Pearson, Tom
2007 IEEE 15TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS, VOLS 1-3, 2007, : 1023 - +
[9] Time-frequency based features for classification of walking patterns
Ibrahim, Ronny K.
Ambikairajah, Eliathamby
Celler, Branko G.
Lovell, Nigel H.
PROCEEDINGS OF THE 2007 15TH INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING, 2007, : 187 - +
[10] Time-Frequency analysis of Photoplethysmogram for measuring Deepness of anesthesia
Al-Taan, Ahmad S.
Stork, Wilhelm
Schumann, S.
Wirth, S.
Hahn, Karla
Guttmann, J.
2013 FIRST INTERNATIONAL SCIENTIFIC CONFERENCE ON ELECTRICAL, COMMUNICATION, COMPUTER, POWER, AND CONTROL ENGINEERING (ICECCPCE'13), 2013, : 119 - 124

← 1 2 3 4 5 →