PMID- 24681668
OWN - NLM
STAT- MEDLINE
DCOM- 20150406
LR  - 20211021
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 14
IP  - 4
DP  - 2014 Mar 27
TI  - A novel approach to ECG classification based upon two-layered HMMs in body sensor 
      networks.
PG  - 5994-6011
LID - 10.3390/s140405994 [doi]
AB  - This paper presents a novel approach to ECG signal filtering and classification. 
      Unlike the traditional techniques which aim at collecting and processing the ECG 
      signals with the patient being still, lying in bed in hospitals, our proposed 
      algorithm is intentionally designed for monitoring and classifying the patient's 
      ECG signals in the free-living environment. The patients are equipped with 
      wearable ambulatory devices the whole day, which facilitates the real-time heart 
      attack detection. In ECG preprocessing, an integral-coefficient-band-stop (ICBS) 
      filter is applied, which omits time-consuming floating-point computations. In 
      addition, two-layered Hidden Markov Models (HMMs) are applied to achieve ECG 
      feature extraction and classification. The periodic ECG waveforms are segmented 
      into ISO intervals, P subwave, QRS complex and T subwave respectively in the 
      first HMM layer where expert-annotation assisted Baum-Welch algorithm is utilized 
      in HMM modeling. Then the corresponding interval features are selected and 
      applied to categorize the ECG into normal type or abnormal type (PVC, APC) in the 
      second HMM layer. For verifying the effectiveness of our algorithm on abnormal 
      signal detection, we have developed an ECG body sensor network (BSN) platform, 
      whereby real-time ECG signals are collected, transmitted, displayed and the 
      corresponding classification outcomes are deduced and shown on the BSN screen.
FAU - Liang, Wei
AU  - Liang W
AD  - Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, 
      Chinese Academy of Sciences, Shenyang 110016, China. weiliang@sia.cn.
FAU - Zhang, Yinlong
AU  - Zhang Y
AD  - Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, 
      Chinese Academy of Sciences, Shenyang 110016, China. zhangyinlong@sia.cn.
FAU - Tan, Jindong
AU  - Tan J
AD  - Department of Mechanical, Aerospace and Biomedical Engineering, University of 
      Tennessee, Knoxville, TN 37996, USA. tan@utk.edu.
FAU - Li, Yang
AU  - Li Y
AD  - Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, 
      Chinese Academy of Sciences, Shenyang 110016, China. liyang@sia.cn.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20140327
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Algorithms
MH  - *Computer Communication Networks
MH  - Computer Simulation
MH  - Electrocardiography/*classification
MH  - *Markov Chains
MH  - Models, Theoretical
MH  - Signal Processing, Computer-Assisted
MH  - Telemetry/*instrumentation
MH  - Time Factors
MH  - Wavelet Analysis
PMC - PMC4029659
EDAT- 2014/04/01 06:00
MHDA- 2015/04/07 06:00
PMCR- 2014/04/01
CRDT- 2014/04/01 06:00
PHST- 2013/12/18 00:00 [received]
PHST- 2014/02/24 00:00 [revised]
PHST- 2014/03/18 00:00 [accepted]
PHST- 2014/04/01 06:00 [entrez]
PHST- 2014/04/01 06:00 [pubmed]
PHST- 2015/04/07 06:00 [medline]
PHST- 2014/04/01 00:00 [pmc-release]
AID - s140405994 [pii]
AID - sensors-14-05994 [pii]
AID - 10.3390/s140405994 [doi]
PST - epublish
SO  - Sensors (Basel). 2014 Mar 27;14(4):5994-6011. doi: 10.3390/s140405994.