A high-speed C++/MEX solution for long-duration arterial blood pressure characteristic locations detection

Homaeinezhad, Mohammad Reza; Ghaffari, Ali; Aghaee, Mohammad; Toosi, H. Najjaran; Rahmani, Reza

doi:10.1016/j.bspc.2011.05.011

Cited by 7 publications

(2 citation statements)

References 33 publications

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“…The ECG is a diagnostic tool which is widely used in ICUs to monitor and assess patient's heart long-term function(s). A large number of methods aimed for detection of non-invasively measured quantities (such as ECG [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], Phonocardiogram (PCG) [20] and Arterial Blood Pressure (ABP) [12,17,21] signals) events have yet been proposed based on mathematical models [10], Hilbert Transform (HT) and first derivative [11][12][13], second order derivative [14], wavelet transform and filter banks [5,15,16], soft computing (Neuro-fuzzy, genetic algorithm) [17,18], Hidden Markov Models (HMM) application [19], etc. Based on a comprehensive literature survey among many documented works, it is seen that several features and extraction (selection) methods have been created and implemented by authors in order for detection and classification of ECG waves.…”

Section: Introductionmentioning

confidence: 99%

QRS complex detection based on simple robust 2-D pictorial-geometrical feature

Sabzevari

Moavenian

2013

Journal of Medical Engineering & Technology

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In this paper a heuristic method aimed for detecting of QRS complexes without any pre-process was developed. All the methods developed in previous studies were used pre-process, the most novelty of this study was suggesting a simple method which did not need any pre-process. Toward this objective, a new simple 2-D geometrical feature space was extracted from the original electrocardiogram (ECG) signal. In this method, a sliding window was moved sample-by-sample on the pre-processed ECG signal. During each forward slide of the analysis window an artificial image was generated from the excerpted segment allocated in the window. Then, a geometrical feature extraction technique based on curve-length and angle of highest point was applied to each image for establishment of an appropriate feature space. Afterwards the K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) and Adaptive Network Fuzzy Inference Systems (ANFIS) were designed and implemented to the ECG signal. The proposed methods were applied to DAY general hospital high resolution holter data. For detection of QRS complex the average values of sensitivity Se = 99.93% and positive predictivity P+ = 99.92% were obtained.

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Section: Introductionmentioning

confidence: 99%

QRS complex detection based on simple robust 2-D pictorial-geometrical feature

Sabzevari

Moavenian

2013

Journal of Medical Engineering & Technology

View full text Add to dashboard Cite

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“…Based on (18), the velocity of moving spins is proportional to phase shift as follows (19) The velocity of spins can be measured in three directions via application of flow sensitive gradients in different directions. This can be performed by applying bipolar gradients in slice selection, phase encoding and frequency encoding directions between the slice select and readout gradients.…”

Section: Flow Quantificationmentioning

confidence: 99%