Software Toolkit For Designing An Artificial Neural Network.

Basically, there are two kinds of artificial neural network (ANN), which can be classified into supervised and unsupervised. Commonly, supervised neural networks are trained or weights adjusted, so that a particular input leads to a specific target output. Generally, the supervised training methods are commonly used in solving most problems. An ANN can be designed, trained, validated and tested by means of the Neural Network Toolbox in Matlab

Detection of Real Time QRS Complex Using Wavelet Transform

This paper presents a novel method for QRS detection. To accomplish this task ECG signal was first filtered by using a third order Savitzky Golay filter. The filtered ECG signal was then preprocessed by a Wavelet based denoising in a real-time fashion to minimize the undefined noise level. R-peak was then detected from denoised signal after wavelet denoising. Windowing mechanism was also applied for finding any missing R-peaks. All the 48 records have been used to test the proposed method. During this testing, 99.97% sensitivity and 99.99% positive predictivity is obtained for QRS complex detection

Prediction of Anomalies for a Dynamic Data System in an Optimized Approach

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P and R Wave Detection in Complete Congenital Atrioventricular Block

Complete atrioventricular block (type III AVB) is characterized by an absence of P wave transmission to ventricles. This implies that QRS complexes are generated in an autonomous way and are not coordinated with P waves. This work introduces a new algorithm for the detection of P waves for this type of pathology using non-invasive electrocardiographic surface leads. The proposed algorithm is divided into three stages. In the first stage, the R waves located by a QRS detector are used to generate the RR series and time references for the other stages of the algorithm. In the second stage, the ventricular activity (QT segment) is removed by using an adaptive filter that obtains an averaged pattern of the QT segment. In the third stage, a new P wave detector is applied to the residual signal obtained after QT cancellation in order to detect P wave locations and get the PP time series. Eight Holter records from patients with congenital type III AVB were used to verify the proposed algorithm. Although further improvements should be made to improve the algorithm¿s performance, the results obtained show high average values of sensitivity (90.52 %) and positive prediction (90.98%)

Improving ECG Classification Accuracy Using an Ensemble of Neural Network Modules

This paper illustrates the use of a combined neural network model based on Stacked Generalization method for classification of electrocardiogram (ECG) beats. In conventional Stacked Generalization method, the combiner learns to map the base classifiers' outputs to the target data. We claim adding the input pattern to the base classifiers' outputs helps the combiner to obtain knowledge about the input space and as the result, performs better on the same task. Experimental results support our claim that the additional knowledge according to the input space, improves the performance of the proposed method which is called Modified Stacked Generalization. In particular, for classification of 14966 ECG beats that were not previously seen during training phase, the Modified Stacked Generalization method reduced the error rate for 12.41% in comparison with the best of ten popular classifier fusion methods including Max, Min, Average, Product, Majority Voting, Borda Count, Decision Templates, Weighted Averaging based on Particle Swarm Optimization and Stacked Generalization

DIAGNOSIS PENYAKIT PARKINSON MELALUI ANALISIS POLA BERJALAN BERDASARKAN VGRF MENGGUNAKAN WAVELET DAN SUPPORT VECTOR MACHINE

Penyakit parkinson atau Parkinson’s Disease (PD) tidak dapat didiagnosis disaat gejala muncul melalui citra medis yang didapatkan dari teknologi pindaian otak menggunakan computed tomography dan magnetic resonance imaging terhadap penderita PD karena tampak normal. Maka dari itu dibutuhkan metode yang dapat digunakan untuk mendiagnosis PD secara dini meskipun penderita PD masih tampak normal. Sehingga ahli medis dan para peneliti PD menyarankan kolaborasi antar bidang ilmu pengetahuan agar penelitian PD menjadi efektif. Diagnosis PD dengan melihat gejala yang muncul merupakan kemungkinan terbaik yang dapat dilakukan untuk mencegah PD berkembang dengan cepat setelah penderita terdiagnosis. Penderita PD bukan hanya memiliki gejala kegoyahan dan kekakuan saja melainkan juga memiliki kelainan bergerak dan kehilangan keseimbangan. Oleh karena itu, penelitian ini dilakukan dengan cara mengklasifikasi rekaman sinyal yang dihasilkan oleh sensor vertical ground reaction force (VGRF) bersumber dari database Physiobank. Sensor VGRF berjumlah 16 sensor dipasang pada kaki saat berjalan agar dapat mendiagnosis PD melalui analisis pola berjalan dengan menggabungkan koefisien wavelet dari hasil dekomposisi sinyal VGRF dan diklasifikasi menggunakan support vector machine (SVM). Penelitian ini menunjukkan bahwa koefisien wavelet adalah ciri yang baik untuk mewakili sinyal VGRF. SVM pada 140 vektor pelatihan dan 139 vektor pengujian mencapai akurasi klasifikasi sebesar 81,29% dengan waktu central processing unit (CPU) selama 80,87 detik sehingga metode ini dapat dipertimbangkan untuk digunakan pada analisis pola berjalan bagi penderita PD berdasarkan rekaman sinyal VGRF. Penelitian ini dapat memperlambat perkembangan penyakit PD karena diagnosis dilakukan secara dini serta memberi kesempatan ahli medis untuk memberikan rekomendasi perawatan setelah penderita PD terdiagnosis

Towards an Intelligent Framework for Pressure-based 3D Curve Drawing

Pen pressure is an input channel typically available in tablet pen device. To date, little attention has been paid to the use of pressure in the domain of graphical interaction, its usage largely limited to drawing and painting program, typically for varying brush characteristic such as stroke width, opacity and color. In this paper, we explore the use of pressure in 3D curve drawing. The act of controlling pressure using pen, pencil and brush in real life appears effortless, but to mimic this natural ability to control pressure using a pressure sensitive pen in the realm of electronic medium is difficult. Previous pressure based interaction work have proposed various signal processing techniques to improve the accuracy in pressure control, but a one-for-all signal processing solution tend not to work for different curve types. We propose instead a framework which applies signal processing techniques tuned to individual curve type. A neural network classifier is used as a curve classifier. Based on the classification, a custom combination of signal processing techniques is then applied. Results obtained point to the feasibility and advantage of the approach.Comment: This paper was rejected from GI 2014. Comment from the chief reviewer:All reviewers noted that the ideas behind this paper were promising, but felt that research was not quite sufficiently developed...Although all agreed that this idea is insightful and has the potential to lead to a valuable contribution,... the idea is not yet sufficiently developed to warrant publicatio

Automated Detection of Atrial Fibrillation Based on Time-Frequency Analysis of Seismocardiograms

In this paper, a novel method to detect atrial fibrillation from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting. After motion artefact removal, in total 119 minutes of AFib data and 126 minutes of sinus rhythm data were considered for automated atrial fibrillation detection. No other arrhythmias were considered in this study. The proposed algorithm requires no direct heartbeat peak detection from the SCG data, which makes it tolerant against interpersonal variations in the SCG morphology, and noise. Furthermore, the proposed method relies solely on SCG and needs no complementary electrocardiography (ECG) to be functional. For the considered data, the detection method performs well even on relatively low quality SCG signals. Using a majority voting scheme which takes 5 randomly selected segments from a signal and classifies these segments using the proposed algorithm, we obtained an average true positive rate of 99.9% and an average true negative rate of 96.4% for detecting atrial fibrillation in leave-one-out cross-validation. The presented work facilitates adoption of MEMS-based heart monitoring devices for arrhythmia detection.</p