Mohammad Javad Mollakazemi

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The purpose of this study is to provide a new method for detecting fetal QRS complexes from non-invasive fetal electrocardiogram (fECG) signal. Despite most of the current fECG processing methods which are based on separation of fECG from maternal ECG (mECG), in this study, fetal heart rate (FHR) can be extracted with high accuracy without separation of(More)
The aim of this study is the intelligent recognition of the fetal heart rate and its R-R intervals from noninvasive fetal electrocardiogram signals. The non-value data was first eliminated and the missing data were regenerated based on the statistical distribution of the data. Then, the power line noise and baseline noise are removed. At the next step, a(More)
The most straightforward method for heart beat estimation is R-peak detection based on an electrocardiogram (ECG) signal. Current R-peak detection methods do not work properly when the ECG signal is contaminated or missing, which leads to the incorrect estimation of the heart rate. This raises the need for reliable algorithms which can locate heart beats in(More)
In this study, a new method for detection of arterial blood pressure pulses (ABP) is presented. The algorithm employs discrete wavelet transform (DWT) decomposition to extract ABP waveform features. In the proposed method, two strategies are used. In the first strategy, the algorithm uses only the DWT coefficients of ABP. The second strategy which is(More)
Introduction: An ECG signal has important information that can help for reflecting cardiac activity of a patient and medical diagnosis. Consistent or periodical heart rhythm disorders can result cardiac arrhythmias so classification algorithm for recognizing arrhythmias with satisfactory accuracy is necessary. Aims: In this study, a robust wavelet based(More)
Introduction: Intelligent patient monitoring has continued to enhance and develop in hospitals from the early stage of monitoring systems. So, practical medical monitoring devices to react to patient conditions and also detect unwanted clinical conditions are very important. Aims: Our algorithm uses pulsatile waveforms and simultaneous ECG in order to(More)
Aims: To address the PhysioNet/Computing in Cardiology (CinC) Challenge 2016 [1], this study aims to discuss a new method to classify PCG signals collected from a variety of clinical or nonclinical environments. Method: The raw PCG data is pre-processed by applying a band pass finite-duration impulse response filter (FIR) and discrete wavelet transform(More)
Heart sound analysis has been a major topic of research during the past few decades. However, necessity for a large reliable and database has been a major concern in these studies. Noting that the current heart sound classification methods do not work properly for noisy signals, the PhysioNet/CinC Challenge 2016 aims at developing the heart sound(More)
Introduction: Coronary artery congestion is a heart disease which causes a lack of oxygen and nutrients in the heart, and is felt as chest pain (ischemia disease). Prolonged ischemia can continue until the cells start to dye, which is called myocardial infraction. Aims: We aim to determine the amount of cardiac tissue damage by multi resolution analysis of(More)
Introduction: In many conditions contaminated signals and noises can distort electrocardiogram (ECG) signals, so synchronously measured signals could enhance analyzing the heart rate variability. Therefore, the algorithm should be able to identify reliable and optimal segment of these multimodal signals in order to accurately locate the heart beats. Aims:(More)
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