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Interactions among physiological mechanisms are abundant in biomedical signals, and they may exist to maintain efficient homeostasis. For example, sympathetic and parasympathetic neural activities interact to either elevate or depress the heart rate, to maintain homeostasis. There has been considerable effort devoted to developing algorithms that can detect interactions between various physiological...
The set partitioning in hierarchical trees (SPIHT) algorithm is very effective and computationally simple technique for image and signal compression. Here the author modified the algorithm which provides even better performance than the SPIHT algorithm. The enhanced set partitioning in hierarchical trees (ESPIHT) algorithm has performance faster than the SPIHT algorithm. In addition, the proposed...
In this paper, we present a novel blind watermarking method with secret key by embedding ECG signals in medical images. The embedding is done when the original image is compressed using the embedded zero-tree wavelet (EZW) algorithm. The extraction process is performed at the decompression time of the watermarked image. Our algorithm has been tested on several CT and MRI images and the peak signal...
This paper is concerned with the problem of localizing the typical features of a signal when it is observed with noise in order to align a set of curves. Structural intensity (SI) is a recent tool that computes the "density" of the location of the modulus maxima of a wavelet representation along various scales in order to identify singularities of an unknown signal. As a contribution to...
In this paper we develop an approach to synthesize a wavelet that matches the ECG signal. Matching a wavelet to a signal of interest has potential advantages in extracting signal features with greater accuracy, particularly when the signal is contaminated with noise. The approach that we have taken is based on the theoretical work done by Chapa and Rao. We have applied their technique to a noise-free...
A new unsupervised and low complexity method for detection of S1 and S2 components of heart sound without the ECG reference is described The most reliable and invariant feature applied in current state-of-the-art of unsupervised heart sound segmentation algorithms is implicitly or explicitly the S1-S2 interval regularity. However; this criterion is inherently prone to noise influence and does not...
A new unsupervised and low complexity method for detection of S1 and S2 components of heart sound without the ECG reference is described The most reliable and invariant feature applied in current state-of-the-art of unsupervised heart sound segmentation algorithms is implicitly or explicitly the S1-S2 interval regularity. However; this criterion is inherently prone to noise influence and does not...
Interactions among physiological mechanisms are abundant in biomedical signals, and they may exist to maintain efficient homeostasis. For example, sympathetic and parasympathetic neural activities interact to either elevate or depress the heart rate, to maintain homeostasis. There has been considerable effort devoted to developing algorithms that can detect interactions between various physiological...
In this paper we develop an approach to synthesize a wavelet that matches the ECG signal. Matching a wavelet to a signal of interest has potential advantages in extracting signal features with greater accuracy, particularly when the signal is contaminated with noise. The approach that we have taken is based on the theoretical work done by Chapa and Rao. We have applied their technique to a noise-free...
In this paper, we present a novel blind watermarking method with secret key by embedding ECG signals in medical images. The embedding is done when the original image is compressed using the embedded zero-tree wavelet (EZW) algorithm. The extraction process is performed at the decompression time of the watermarked image. Our algorithm has been tested on several CT and MRI images and the peak signal...
Computer-aided bedside patient monitoring requires real-time analysis of vital functions. On-line Holter monitors need reliable and quick algorithms to perform all the necessary signal processing tasks. This paper presents the methods that were conceptualized and implemented at the development of such a monitoring system at Medical Clinic No. 4 of Targu-Mures. The system performs the following ECG...
Guaranteeing reconstruction quality in ECG lossy compression is essential to obtain signals useful from a clinical point of view. In this paper we discuss the advantages and drawbacks of using two very well known mathematical error measures (PRD and RMS) in order to guarantee quality in threshold wavelet compression codecs that work segmenting the signal into blocks. We use two different error indices...
In this paper a novel approach for cardiac arrhythmias detection is proposed. The proposed method is based on using independent component analysis (ICA) and wavelet transform to extract important features. Using the extracted features different machine learning classification schemas, MLP and RBF neural networks and K-nearest neighbor, are used to classify 274 instance signals from the MIT-BIH database...
The set partitioning in hierarchical trees (SPIHT) algorithm is very effective and computationally simple technique for image and signal compression. Here the author modified the algorithm which provides even better performance than the SPIHT algorithm. The enhanced set partitioning in hierarchical trees (ESPIHT) algorithm has performance faster than the SPIHT algorithm. In addition, the proposed...
This paper is concerned with the problem of localizing the typical features of a signal when it is observed with noise in order to align a set of curves. Structural intensity (SI) is a recent tool that computes the "density" of the location of the modulus maxima of a wavelet representation along various scales in order to identify singularities of an unknown signal. As a contribution to...
A novel automatic QRS detection algorithm that is based on a wavelet pre-filter and an adaptive threshold technique is presented. The algorithm utilizes a bi-orthogonal wavelet filter to de-noise the ECG signal. The QRS complexes are then identified by computing the first derivative of the signal and applying a set of adaptive thresholds that are not limited to a strict range. QRS complexes are identified...
An approach for designing multiwavelets is introduced, for use in cardiac signal processing. The parameterization of the class of multiwavelets is in terms of associated FIR polyphase all-pass filters. Orthogonality and a balanced vanishing moment of order 1 are built into the parameterization. An optimization criterion is developed to associate the wavelets with different meaningful segments of a...
A novel automatic QRS detection algorithm that is based on a wavelet pre-filter and an adaptive threshold technique is presented. The algorithm utilizes a bi-orthogonal wavelet filter to de-noise the ECG signal. The QRS complexes are then identified by computing the first derivative of the signal and applying a set of adaptive thresholds that are not limited to a strict range. QRS complexes are identified...
Guaranteeing reconstruction quality in ECG lossy compression is essential to obtain signals useful from a clinical point of view. In this paper we discuss the advantages and drawbacks of using two very well known mathematical error measures (PRD and RMS) in order to guarantee quality in threshold wavelet compression codecs that work segmenting the signal into blocks. We use two different error indices...
In this paper a novel approach for cardiac arrhythmias detection is proposed. The proposed method is based on using independent component analysis (ICA) and wavelet transform to extract important features. Using the extracted features different machine learning classification schemas, MLP and RBF neural networks and K-nearest neighbor, are used to classify 274 instance signals from the MIT-BIH database...
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