Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines.

The use of wearable recorders for long-term monitoring of physiological parameters has increased in the last few years. The ambulatory electrocardiogram (A-ECG) signals of five healthy subjects with four body movements or physical activities (PA)-left arm up down, right arm up down, waist twisting and walking-have been recorded using a wearable ECG recorder. The classification of these four PAs has been performed using neuro-fuzzy classifier (NFC) and support vector machines (SVM). The PA classification is based on the distinct, time-frequency features of the extracted motion artifacts contained in recorded A-ECG signals. The motion artifacts in A-ECG signals have been separated first by the discrete wavelet transform (DWT) and the time-frequency features of these motion artifacts have then been extracted using the Gabor transform. The Gabor energy feature vectors have been fed to the NFC and SVM classifiers. Both the classifiers have achieved a PA classification accuracy of over 95% for all subjects.

RPCA-based detection and quantification of motion artifacts in ECG signals.

In this paper, a recursive principal component analysis (RPCA)-based algorithm is applied for detecting and quantifying the motion artifact episodes encountered in an ECG signal. The motion artifact signal is synthesized by low-pass filtering a random noise signal with different spectral ranges of LPF (low pass filter): 0-5 Hz, 0-10 Hz, 0-15 Hz and 0-20 Hz. Further, the analysis of the algorithm is carried out for different values of SNR levels and forgetting factors (α) of an RPCA algorithm. The algorithm derives an error signal, wherever a motion artifact episode (noise) is present in the entire ECG signal with 100% accuracy. The RPCA error magnitude is almost zero for the clean signal portion and considerably high wherever the motion artifacts (noisy episodes) are encountered in the ECG signals. Further, the general trend of the algorithm is to produce a smaller magnitude of error for higher SNR (i.e. low level of noise) and vice versa. The quantification of the RPCA algorithm has been made by applying it over 25 ECG data-sets of different morphologies and genres with three different values of SNRs for each forgetting factor and for each of four spectral ranges.

Impact of ambulation in wearable-ECG.

Ambulatory ElectroCardioGram (ECG) analysis is adversely affected by motion artifacts induced due to body movements. Knowledge of the extent of motion artifacts could facilitate better ECG analysis. In this paper, our purpose is to determine the impact of body movement kinematics on the extent of ECG motion artifact by defining a notion called impact signal. Two approaches have been adopted in this paper to validate our experiments. One of them involves measuring local acceleration using motion sensors at appropriate body positions, in conjunction with the ECG, while performing routine activities at different intensity levels. The other method consists of ECG acquisition during Treadmill testing at controlled speeds and fixed duration. Data has been acquired from both healthy subjects as well as patients with suspected cardio-vascular disorders. In case of patients, the treadmill tests were carried out under the supervision of a cardiologist. We demonstrate that the impact signal shows a proportional increase with the increasing activity levels. The measured accelerations obtained are also found to be well correlated with the impact signal. The impact analysis thus indicates the suitability of the proposed method for quantification of body movement kinematics from the ECG signal itself, even in the absence of any accelerometer sensors. Such quantification would also help in automatic documentation of patient activity levels, which could aid in better interpretation of ambulatory ECG.

Impact analysis of body movement in ambulatory ECG.

Ambulatory ECG analysis is adversely affected by motion artifacts induced due to body movements. Knowledge of the extent of motion artifacts facilitates better ECG analysis. In [1], an unsupervised method using recursive principal component analysis (RPCA) was used to detect transitions between body movements. In this paper, we endeavour to quantify the impact of various types of body movements on the extent of ECG motion artifact using the RPCA error signal. For this purpose, acceleration data from different body parts i.e. arm(s), leg and waist, have been obtained using commercially available motion sensors, in conjunction with ECG signal, while carrying out routine body movement activities like climbing stairs, walking, twisting, and arm movements, at three different intensity levels: slow, medium and fast. The acceleration magnitudes and the RPCA error sequence are found to be well correlated, thus validating the body movement impact analysis, and also indicating the suitability of the method for quantification of body movement kinematics from the ECG signal itself in the absence of any accelerometer sensors.

Transition detection in body movement activities for wearable ECG.

It has been shown by Pawar et al. (2007) that the motion artifacts induced by body movement activity (BMA) in a single-lead wearable electrocardiogram (ECG) signal recorder, while monitoring an ambulatory patient, can be detected and removed by using a principal component analysis (PCA)-based classification technique. However, this requires the ECG signal to be temporally segmented so that each segment comprises of artifacts due to a single type of body movement activity. In this paper, we propose a simple, recursively updated PCA-based technique to detect transitions wherever the type of body movement is changed.

Body movement activity recognition for ambulatory cardiac monitoring.

Wearable electrocardiogram (W-ECG) recorders are increasingly in use by people suffering from cardiac abnormalities who also choose to lead an active lifestyle. The challenge presently is that the ECG signal is influenced by motion artifacts induced by body movement activity (BMA) of the wearer. The usual practice is to develop effective filtering algorithms which will eliminate artifacts. Instead, our goal is to detect the motion artifacts and classify the type of BMA from the ECG signal itself. We have recorded the ECG signals during specified BMAs, e.g., sitting still, walking, movements of arms and climbing stairs, etc. with a single-lead system. The collected ECG signal during BMA is presumed to be an additive mix of signals due to cardiac activities, motion artifacts and sensor noise. A particular class of BMA is characterized by applying eigen decomposition on the corresponding ECG data. The classification accuracies range from 70% to 98% for various class combinations of BMAs depending on their uniqueness based on this technique. The above classification is also useful for analysis of P and T waves in the presence of BMA.

Analysis of ambulatory ECG signal.

Ambulatory electrocardiogram (ECG) recorders are increasingly in use by people suffering from cardiac abnormalities. However, the ECG signal acquired by the ambulatory recorder is influenced by motion artifacts induced by any body movement activity (BMA). The goal of the paper is to demonstrate that it is possible to determine the BMA from the motion artifacts in the ECG signal itself. The ECG signal during a specific BMA is presumed to be an additive mix of signals due to cardiac activities, motion artifacts induced due to the BMA and sensor noise. We propose to characterize and determine the BMA from the corresponding motion artifact data in the ECG signal itself. The proposed technique is useful for removal of motion artifacts from the ECG signals for ambulatory cardiac monitoring.