12 Biomedical Engineering Fundamentals
601
12.9.1 Detection of Heart-Related Disorders
The heart-related signals are very useful to detect cardiovascular diseases. These
detections are generally based on heart sounds and ECG signals. The detection
of congestive heart failure (CHF) is carried out using eigenvalue decomposition
from HRV signals extracted with ECG signals [55, 56]. The heart valve disorders
can be classified with the method based on tunable-Q wavelet transform (TQWT)
[57]. The diagnosis of arrhythmia using flexible analytic wavelet transform (FAWT)
from ECG signals has significant importance [58]. The FAWT method is used for
the detection of myocardial infarction (MI), which is a condition that indicates
injury of the heart cell [59]. The automated detection system of CAD is developed
with FAWT method using ECG signals [60]. In another work [61], the detection
of CAD is also possible with HRV signals involving the FAWT. A method for
diabetic patients using RR interval signals obtained from ECG signal is developed
for screening [23].
12.9.2 Detection of Brain-Related Diseases
The brain-related diseases and disorders such as epilepsy, Alzheimer’s, Parkinson’s,
and sleep disorder can be detected by EEG and MEG signals. The detection
technique based on EMD method using EEG signals for epileptic seizure has been
proposed [62]. The phase space representation of intrinsic mode functions has
been also utilized to classify epileptic seizure EEG signals [63]. The second-order
difference plot of intrinsic mode functions has been also used for epileptic seizure
classification [64]. The entropy of intrinsic mode functions has been used for the
automated detection of focal EEG signals [65]. The detection of ictal EEG signals
using fractional linear prediction has been also proposed [66]. The sleep stages have
been also classified using time-frequency image of EEG signals [67]. The iterative
filtering method has been also used to develop an automated system for sleep stage
classification [68].
12.9.3 Detection of Neuromuscular Diseases
The diagnosis of neuromuscular diseases has been also possible using computeraided method. The technique based on wavelet neural network applied on EMG
signals has been proposed for neuromuscular disorder detection [69]. A technique
based on discrete wavelet transform for EMG signal classification with comparison
of decision tree algorithms has been proposed [70]. The detection of muscle fatigue
using EMG signals with time-frequency methods has been presented [71]. The
fatigue during dynamic contractions of the muscle has been also detected using
601
12.9.1 Detection of Heart-Related Disorders
The heart-related signals are very useful to detect cardiovascular diseases. These
detections are generally based on heart sounds and ECG signals. The detection
of congestive heart failure (CHF) is carried out using eigenvalue decomposition
from HRV signals extracted with ECG signals [55, 56]. The heart valve disorders
can be classified with the method based on tunable-Q wavelet transform (TQWT)
[57]. The diagnosis of arrhythmia using flexible analytic wavelet transform (FAWT)
from ECG signals has significant importance [58]. The FAWT method is used for
the detection of myocardial infarction (MI), which is a condition that indicates
injury of the heart cell [59]. The automated detection system of CAD is developed
with FAWT method using ECG signals [60]. In another work [61], the detection
of CAD is also possible with HRV signals involving the FAWT. A method for
diabetic patients using RR interval signals obtained from ECG signal is developed
for screening [23].
12.9.2 Detection of Brain-Related Diseases
The brain-related diseases and disorders such as epilepsy, Alzheimer’s, Parkinson’s,
and sleep disorder can be detected by EEG and MEG signals. The detection
technique based on EMD method using EEG signals for epileptic seizure has been
proposed [62]. The phase space representation of intrinsic mode functions has
been also utilized to classify epileptic seizure EEG signals [63]. The second-order
difference plot of intrinsic mode functions has been also used for epileptic seizure
classification [64]. The entropy of intrinsic mode functions has been used for the
automated detection of focal EEG signals [65]. The detection of ictal EEG signals
using fractional linear prediction has been also proposed [66]. The sleep stages have
been also classified using time-frequency image of EEG signals [67]. The iterative
filtering method has been also used to develop an automated system for sleep stage
classification [68].
12.9.3 Detection of Neuromuscular Diseases
The diagnosis of neuromuscular diseases has been also possible using computeraided method. The technique based on wavelet neural network applied on EMG
signals has been proposed for neuromuscular disorder detection [69]. A technique
based on discrete wavelet transform for EMG signal classification with comparison
of decision tree algorithms has been proposed [70]. The detection of muscle fatigue
using EMG signals with time-frequency methods has been presented [71]. The
fatigue during dynamic contractions of the muscle has been also detected using