Fully parallel ANN-based arrhythmia classifier on a single-chip FPGA: FPAAC

Ahmet Turan ÖZDEMİR; Kenan DANIŞMAN

Makale detayları

Fully parallel ANN-based arrhythmia classifier on a single-chip FPGA: FPAAC

Yazarlar:

Ahmet Turan ÖZDEMİR | Kenan DANIŞMAN

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2011 Vol.19 Issue 4, pp.667-687

Belge Tipi:

Makale

Yayın Tarihi:

3/2011

Belge Dili:

Türkçe

Document ID (Org No.):

2000743785

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

Recognition of cardiac arrhythmias by electrocardiogram  (ECG) is an important issue for diagnosis of cardiac abnormalities.  Many studies on recognition of cardiac arrhythmias by ECG, using  various techniques, have been performed in the past 20 years.  Artificial neural networks (ANNs) are the most widely used tool in  medical diagnosis systems (MDS) because of their powerful prediction  characteristics.  An ANN model is inspired by real biological neural  networks, with a parallel structure that is potentially fast for  computation.  However, the suggested ANN architectures in the  literature can only be run sequentially, on powerful processors, due  to their complexity.  Our approach enables the implementation of a  simple ANN architecture with lower requirements for hardware  resources.  The features of the ECG signal are reduced dramatically  using principle component analysis (PCA) while keeping the error rate  of the ANN at an acceptable level, near 5%.  To enable the  implementation of real ANN models on parallel devices, the features of  the ECG signal that are applied to the ANN inputs must be reduced.  In  this study, field programmable gate arrays (FPGA) implementation of a  fully parallel, fault-tolerant ANN for ECG arrhythmia classification  (FPAAC) is realized.  An ANN model, which consists of 8 inputs, a  hidden layer with 2 neurons, and 1 output neuron, is implemented on an  FPGA using IEEE-754 32-bit floating-point numerical representation.  FPAAC classifies 3 classes of arrhythmia, premature ventricular  contraction (PVC), fusion (F), and normal (N) beats, and its accuracy  is 97.66%.  The ECG records used in this work were taken from the  MIT-BIH arrhythmia database.

 

Recognition of cardiac arrhythmias by electrocardiogram  (ECG) is an important issue for diagnosis of cardiac abnormalities.  Many studies on recognition of cardiac arrhythmias by ECG, using  various techniques, have been performed in the past 20 years.  Artificial neural networks (ANNs) are the most widely used tool in  medical diagnosis systems (MDS) because of their powerful prediction  characteristics.  An ANN model is inspired by real biological neural  networks, with a parallel structure that is potentially fast for  computation.  However, the suggested ANN architectures in the  literature can only be run sequentially, on powerful processors, due  to their complexity.  Our approach enables the implementation of a  simple ANN architecture with lower requirements for hardware  resources.  The features of the ECG signal are reduced dramatically  using principle component analysis (PCA) while keeping the error rate  of the ANN at an acceptable level, near 5%.  To enable the  implementation of real ANN models on parallel devices, the features of  the ECG signal that are applied to the ANN inputs must be reduced.  In  this study, field programmable gate arrays (FPGA) implementation of a  fully parallel, fault-tolerant ANN for ECG arrhythmia classification  (FPAAC) is realized.  An ANN model, which consists of 8 inputs, a  hidden layer with 2 neurons, and 1 output neuron, is implemented on an  FPGA using IEEE-754 32-bit floating-point numerical representation.  FPAAC classifies 3 classes of arrhythmia, premature ventricular  contraction (PVC), fusion (F), and normal (N) beats, and its accuracy  is 97.66%.  The ECG records used in this work were taken from the  MIT-BIH arrhythmia database.

Makale detayları

Fully parallel ANN-based arrhythmia classifier on a single-chip FPGA: FPAAC

Yazarlar: Ahmet Turan ÖZDEMİR | Kenan DANIŞMAN

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2011 Vol.19 Issue 4, pp.667-687

Belge Tipi: Makale

Yayın Tarihi: 3/2011

Belge Dili: Türkçe

Document ID (Org No.): 2000743785

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Ahmet Turan ÖZDEMİR | Kenan DANIŞMAN

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2011 Vol.19 Issue 4, pp.667-687

Belge Tipi:

Makale

Yayın Tarihi:

3/2011

Belge Dili:

Türkçe

Document ID (Org No.):

2000743785

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632