Heart murmurs extraction using the complete Ensemble Empirical Mode Decomposition and the Pearson distance metric

Jusak, Jusak; Puspasari, Ira; Susanto, Pauladie

doi:10.1109/icts.2016.7910288

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…Salman et al [97] used EMD for removal of white, colored, exponential, and alpha-stable noise and showed that EMD is superior when compared with wavelet and total variation denoising methods. Heart murmurs were detected by Jusak et al [98] using complete ensemble empirical mode decomposition (CEEMD, another variant of standard EMD) and the Pearson distance metric. Authors concluded that CEEMD is computationally more complex as it extracts more modes in comparison to EMD.…”

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

“…Studies in [86,87] focus on abnormal heart sounds whereas various pathological states like regurgitation and stenosis are investigated in [91,92,100]. Likewise, murmurs have been taken into account in [94,98,100].…”

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

“…Only the works presented in [91,100] are evaluated on the publicly available E-General Medical dataset. Likewise, systems reported in [83,97,98,101] employed the University of Michigan dataset while PASCAL CHSC 2011 dataset is considered in [95]. Standard metrics of accuracy, specificity, sensitivity, etc.…”

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

“…EMD algorithm is compared with other time-frequency algorithms like wavelet and DWT in [87,88,101]. Studies reported in [97,98] used SNR and SNR while [95] employed the average detection rate. Gavrovska et al [88] compared DWT, EMD, and EEMD, and concluded that EEMD reports the best performance among the three.…”

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

See 3 more Smart Citations

Localization and classification of heart beats in phonocardiography signals —a comprehensive review

Ismail

Siddiqi

Akram

2018

EURASIP J. Adv. Signal Process.

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Phonocardiogram (PCG) signal represents recording of sounds and murmurs resulting from heart auscultation. Analysis of these PCG signals is critical in diagnosis of different heart diseases. Over the years, a variety of methods have been proposed for automatic analysis of PCG signals in time, frequency, and time-frequency domains. This paper presents a comprehensive survey of different methods proposed for automatic analysis of PCG signals with the objective to evaluate the current state-of-the-art and to determine the potential domains of effective analysis. An important aspect of our contribution is that the review is carried out as a function of domains of analysis rather than simply discussing different methods. Our method further splits analysis into pre-processing, localization, and classification, and details are presented in terms of techniques and classifiers used during these phases. Finally, results are summarized for normal heart beat, noisy heart beat, and different pathologies using metrices like accuracy and detection rate. In addition to time and frequency domain, time-frequency based methods including wavelet, empirical mode decomposition (EMD) and time-frequency representation (TFR) are selected for detailed analysis. The review concludes that the time-frequency representations like EMD and wavelets represent areas of exploration in future along with perspective of using different time-frequency techniques together.

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Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

Section: Emd and Hilbert-huang Transformmentioning

confidence: 99%

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Localization and classification of heart beats in phonocardiography signals —a comprehensive review

Ismail

Siddiqi

Akram

2018

EURASIP J. Adv. Signal Process.

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“…Penelitian lain melakukan identifikasi sinyal suara jantung dengan menerapkan metode Ensemble Empirical Mode Decomposition (EEMD) dan Complete Ensemble Empirical Mode Decomposition (CEEMD) [6]. Penelitian ini menggunakan metode CEEMD untuk melakukan ekstraksi murmur dari sinyal jantung asli secara signifikan.…”

Section: Pendahuluanunclassified

Model Identifkasi Sinyal Jantung Pertama (S1) dan Sinyal Jantung Kedua (S2) pada Janin

Puspasari¹,

Jusak²,

Kusumawati³

et al. 2020

JRE

Self Cite

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Proses identifikasi sinyal suara jantung janin sangat diperlukan dalam mengenali fungsi jantung bawaan yang disebabkan oleh banyak faktor, misalnya faktor keturunan dan asupan makanan ibu yang mengandung. Penelitian ini mengembangkan metode pemrosesan sinyal jantung untuk memisahkan sinyal phonocardiogram janin normal dari noise dengan memanfaatkan algoritma Complete Empirical Mode Decomposition (CEEMD) yang diintegrasikan dengan Pearson Distance Metric. Sinyal jantung yang telah terpisahkan dari noise, selanjutnya diolah dengan menggunakan persamaan Energi Shannon dengan tujuan untuk mempertajam intensitas sinyal jantung pertama (S1) dan sinyal jantung kedua (S2), tetapi pada saat yang sama menekan intensitas sisa-sisa noise di dalam sinyal. Berdasarkan hasil pengujian pada 75 siklus suara jantung janin normal, model yang telah dikembangkan mampu mengidentifikasi sinyal suara S1 dan sinyal suara S2, durasi waktu T11 (S1-S1), dan durasi waktu T12 (S1-S2). Seluruh pemrosesan sinyal jantung dilakukan dalam domain waktu. Durasi rata-rata T11 dan durasi ratarata T12 dalam penelitian ini dapat digunakan sebagai acuan untuk menentukan durasi sinyal suara jantung janin normal.

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Anomaly Detection in Phonocardiogram Employing Deep Learning

Sujadevi

Ravi

Sankar

2018

Advances in Intelligent Systems and Computing

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Heart murmurs extraction using the complete Ensemble Empirical Mode Decomposition and the Pearson distance metric

Cited by 9 publications

References 10 publications

Localization and classification of heart beats in phonocardiography signals —a comprehensive review

Localization and classification of heart beats in phonocardiography signals —a comprehensive review

Model Identifkasi Sinyal Jantung Pertama (S1) dan Sinyal Jantung Kedua (S2) pada Janin

Anomaly Detection in Phonocardiogram Employing Deep Learning

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