# Automated Arrhythmia Detection Based on RR Intervals

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methods

#### 2.1. Electrocardiogram Data

- 51–60 years representing 19.82%;
- 61–70 years representing 24.38%;
- 71–80 years representing 16.90%.

#### 2.2. QRS Detection

#### 2.3. Data Partitioning and Patient Scrambling

#### 2.4. Detrending

#### 2.5. Round Robin Windowing and Puncturing

#### 2.6. ResNet 10-Fold and Cross-Validation

#### 2.7. Result Analysis Methods

## 3. Results

## 4. Discussion

#### 4.1. Limitations

#### 4.2. Future Work

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

ACC | Accuracy |

AFL | Atrial Flutter |

AFIB | Atrial Fibrillation |

AI | Artificial Intelligence |

API | Application Programming Interface |

AVN | AtrioVentricular Node |

CAD | Computer-Aided-Diagnosis |

DL | Deep Learning |

DB | Database |

DL | Deep Learning |

ECG | Electrocardiogram |

FN | False Negative |

FP | False Positive |

LSTM | Long Short-Term Memory |

NSR | Normal Sinus Rhythm |

ResNet | Residual Neural Network |

ROC | Receiver Operating Characteristic |

SAN | SinoAtrial Node |

SEN | Sensitivity |

SPE | Specificity |

TN | True Negative |

TP | True Positive |

## References

- Desa. United nations department of economic and social affairs, population division. world population prospects: The 2015 revision, key findings and advance tables. In Technical Report: Working Paper No. ESA/P/WP. 241; United Nations: New York, NY, USA, 2015. [Google Scholar]
- Najarian, K.; Splinter, R. Biomedical Signal and Image Processing; CRC Press: Boca Raton, FL, USA, 2005. [Google Scholar]
- Chow, G.V.; Marine, J.E.; Fleg, J.L. Epidemiology of arrhythmias and conduction disorders in older adults. Clin. Geriatr. Med.
**2012**, 28, 539–553. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Kurian, T.; Ambrosi, C.; Hucker, W.; Fedorov, V.V.; Efimov, I.R. Anatomy and electrophysiology of the human AV node. Pacing Clin. Electrophysiol.
**2010**, 33, 754–762. [Google Scholar] [CrossRef] [Green Version] - Waldo, A.L. Atrial fibrillation and atrial flutter: Two sides of the same coin! Int. J. Cardiol.
**2017**, 240, 251–252. [Google Scholar] [CrossRef] [PubMed] - Waldo, A.L.; Feld, G.K. Inter-relationships of atrial fibrillation and atrial flutter: Mechanisms and clinical implications. J. Am. Coll. Cardiol.
**2008**, 51, 779–786. [Google Scholar] [CrossRef] [Green Version] - Rahman, F.; Wang, N.; Yin, X.; Ellinor, P.T.; Lubitz, S.A.; LeLorier, P.A.; McManus, D.D.; Sullivan, L.M.; Seshadri, S.; Vasan, R.S.; et al. Atrial flutter: Clinical risk factors and adverse outcomes in the Framingham Heart Study. Heart Rhythm
**2016**, 13, 233–240. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Acharya, U.R.; Krishnan, S.M.; Spaan, J.A.; Suri, J.S. Advances in Cardiac Signal Processing; Springer: Berlin/Heidelberg, Germany, 2007. [Google Scholar]
- Silverman, M.E.; Willis Hurst, J. Willem Einthoven—The father of electrocardiography. Clin. Cardiol.
**1992**, 15, 785–787. [Google Scholar] [CrossRef] - Wenger, W.; Kligfield, P. Variability of precordial electrode placement during routine electrocardiography. J. Electrocardiol.
**1996**, 29, 179–184. [Google Scholar] [CrossRef] - Martínez, J.P.; Almeida, R.; Olmos, S.; Rocha, A.P.; Laguna, P. A wavelet-based ECG delineator: Evaluation on standard databases. IEEE Trans. Biomed. Eng.
**2004**, 51, 570–581. [Google Scholar] [CrossRef] [PubMed] - Xu, X.; Liu, Y. ECG QRS complex detection using slope vector waveform (SVW) algorithm. In Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Francisco, CA, USA, 1–5 September 2004; Volume 2, pp. 3597–3600. [Google Scholar]
- Lashgari, E.; Liang, D.; Maoz, U. Data Augmentation for Deep-Learning-Based Electroencephalography. J. Neurosci. Methods
**2020**, 346, 108885. [Google Scholar] [CrossRef] - Johnson, J.M.; Khoshgoftaar, T.M. Survey on deep learning with class imbalance. J. Big Data
**2019**, 6, 27. [Google Scholar] [CrossRef] - Zheng, J.; Zhang, J.; Danioko, S.; Yao, H.; Guo, H.; Rakovski, C. A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. Sci. Data
**2020**, 7, 48. [Google Scholar] [CrossRef] [Green Version] - Demski, A.; Soria, M.L. Ecg-kit: A Matlab toolbox for cardiovascular signal processing. J. Open Res. Softw.
**2016**, 4, e8. [Google Scholar] - Fushiki, T. Estimation of prediction error by using K-fold cross-validation. Stat. Comput.
**2011**, 21, 137–146. [Google Scholar] [CrossRef] - Faust, O.; Barika, R.; Shenfield, A.; Ciaccio, E.J.; Acharya, U.R. Accurate detection of sleep apnea with long short-term memory network based on RR interval signals. Knowl.-Based Syst.
**2021**, 212, 106591. [Google Scholar] [CrossRef] - Fisher, A.C.; Eleuteri, A.; Groves, D.; Dewhurst, C.J. The Ornstein–Uhlenbeck third-order Gaussian process (OUGP) applied directly to the un-resampled heart rate variability (HRV) tachogram for detrending and low-pass filtering. Med. Biol. Eng. Comput.
**2012**, 50, 737–742. [Google Scholar] [CrossRef] [Green Version] - Clifford, G.D.; Azuaje, F.; Mcsharry, P. ECG statistics, noise, artifacts, and missing data. Adv. Methods Tools Ecg Data Anal.
**2006**, 6, 18. [Google Scholar] - Laguna, P.; Moody, G.B.; Mark, R.G. Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals. IEEE Trans. Biomed. Eng.
**1998**, 45, 698–715. [Google Scholar] [CrossRef] - Ismail Fawaz, H.; Forestier, G.; Weber, J.; Idoumghar, L.; Muller, P.A. Deep learning for time series classification: A review. Data Min. Knowl. Discov.
**2019**, 33, 917–963. [Google Scholar] [CrossRef] [Green Version] - Chollet, F. Keras. 2015. Available online: https://github.com/fchollet/keras (accessed on 7 August 2021).
- Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, G.S.; Davis, A.; Dean, J.; Devin, M.; et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. 2015. Available online: tensorflow.org (accessed on 7 August 2021).
- Hanin, B. Which neural net architectures give rise to exploding and vanishing gradients? arXiv
**2018**, arXiv:1801.03744. [Google Scholar] - Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett.
**2006**, 27, 861–874. [Google Scholar] [CrossRef] - Gómez, R. Understanding Categorical Cross-Entropy Loss, Binary Cross-Entropy Loss, Softmax Loss, Logistic Loss, Focal Loss and All Those Confusing Names. 2018. Available online: https://gombru.github.io/2018/05/23/cross_entropy_loss/ (accessed on 29 March 2019).
- Kingma, D.P.; Ba, J. Adam: A method for stochastic optimization. arXiv
**2014**, arXiv:1412.6980. [Google Scholar] - Ivanovic, M.D.; Atanasoski, V.; Shvilkin, A.; Hadzievski, L.; Maluckov, A. Deep Learning Approach for Highly Specific Atrial Fibrillation and Flutter Detection based on RR Intervals. In Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23–27 July 2019; pp. 1780–1783. [Google Scholar]
- Faust, O.; Acharya, U.R. Automated classification of five arrhythmias and normal sinus rhythm based on RR interval signals. Expert Syst. Appl.
**2021**, 181, 115031. [Google Scholar] [CrossRef] - Fujita, H.; Cimr, D. Computer aided detection for fibrillations and flutters using deep convolutional neural network. Inf. Sci.
**2019**, 486, 231–239. [Google Scholar] [CrossRef] - Faust, O.; Shenfield, A.; Kareem, M.; San, T.R.; Fujita, H.; Acharya, U.R. Automated detection of atrial fibrillation using long short-term memory network with RR interval signals. Comput. Biol. Med.
**2018**, 102, 327–335. [Google Scholar] [CrossRef] [Green Version] - Acharya, U.R.; Fujita, H.; Lih, O.S.; Hagiwara, Y.; Tan, J.H.; Adam, M. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf. Sci.
**2017**, 405, 81–90. [Google Scholar] [CrossRef] - Henzel, N.; Wróbel, J.; Horoba, K. Atrial fibrillation episodes detection based on classification of heart rate derived features. In Proceedings of the 2017 MIXDES-24th International Conference Mixed Design of Integrated Circuits and Systems, Bydgoszcz, Poland, 22–24 June 2017; pp. 571–576. [Google Scholar]
- Desai, U.; Martis, R.J.; Acharya, U.R.; Nayak, C.G.; Seshikala, G.; Shetty, K.R. Diagnosis of multiclass tachycardia beats using recurrence quantification analysis and ensemble classifiers. J. Mech. Med. Biol.
**2016**, 16, 1640005. [Google Scholar] [CrossRef] - Acharya, U.R.; Fujita, H.; Adam, M.; Lih, O.S.; Hong, T.J.; Sudarshan, V.K.; Koh, J.E. Automated characterization of arrhythmias using nonlinear features from tachycardia ECG beats. In Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary, 9–12 October 2016; pp. 000533–000538. [Google Scholar]
- Hamed, I.; Owis, M.I. Automatic arrhythmia detection using support vector machine based on discrete wavelet transform. J. Med. Imaging Health Inform.
**2016**, 6, 204–209. [Google Scholar] [CrossRef] - Xia, Y.; Wulan, N.; Wang, K.; Zhang, H. Detecting atrial fibrillation by deep convolutional neural networks. Comput. Biol. Med.
**2018**, 93, 84–92. [Google Scholar] [CrossRef] - Petrėnas, A.; Marozas, V.; Sörnmo, L. Low-complexity detection of atrial fibrillation in continuous long-term monitoring. Comput. Biol. Med.
**2015**, 65, 184–191. [Google Scholar] [CrossRef] - Zhou, X.; Ding, H.; Ung, B.; Pickwell-MacPherson, E.; Zhang, Y. Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy. Biomed. Eng. Online
**2014**, 13, 18. [Google Scholar] [CrossRef] [Green Version] - Muthuchudar, A.; Baboo, S.S. A study of the processes involved in ECG signal analysis. Int. J. Sci. Res. Publ.
**2013**, 3, 1–5. [Google Scholar] - Yuan, C.; Yan, Y.; Zhou, L.; Bai, J.; Wang, L. Automated atrial fibrillation detection based on deep learning network. In Proceedings of the 2016 IEEE International Conference on Information and Automation (ICIA), Ningbo, China, 1–3 August 2016; pp. 1159–1164. [Google Scholar]
- Pudukotai Dinakarrao, S.M.; Jantsch, A. ADDHard: Arrhythmia detection with digital hardware by learning ECG signal. In Proceedings of the 2018 on Great Lakes Symposium on VLSI, Chicago, IL, USA, 23–25 May 2018; pp. 495–498. [Google Scholar]
- Salem, M.; Taheri, S.; Yuan, J. ECG Arrhythmia Classification Using Transfer Learning from 2-Dimensional Deep CNN Features. In Proceedings of the 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), Cleveland, OH, USA, 17–19 October 2018; pp. 1–4. [Google Scholar]
- Kareem, M.; Lei, N.; Ali, A.; Ciaccio, E.J.; Acharya, U.R.; Faust, O. A review of patient-led data acquisition for atrial fibrillation detection to prevent stroke. Biomed. Signal Process. Control
**2021**, 69, 102818. [Google Scholar] [CrossRef] - Faust, O.; Lei, N.; Chew, E.; Ciaccio, E.J.; Acharya, U.R. A smart service platform for cost efficient cardiac health monitoring. Int. J. Environ. Res. Public Health
**2020**, 17, 6313. [Google Scholar] [CrossRef] - Yang, C.; Veiga, C.; Rodriguez-Andina, J.J.; Farina, J.; Iniguez, A.; Yin, S. Using PPG signals and wearable devices for atrial fibrillation screening. IEEE Trans. Ind. Electron.
**2019**, 66, 8832–8842. [Google Scholar] [CrossRef] - Guo, Y.; Wang, H.; Zhang, H.; Liu, T.; Liang, Z.; Xia, Y.; Yan, L.; Xing, Y.; Shi, H.; Li, S.; et al. Mobile photoplethysmographic technology to detect atrial fibrillation. J. Am. Coll. Cardiol.
**2019**, 74, 2365–2375. [Google Scholar] [CrossRef] - Jafarifarmand, A.; Badamchizadeh, M.A.; Khanmohammadi, S.; Nazari, M.A.; Tazehkand, B.M. A new self-regulated neuro-fuzzy framework for classification of EEG signals in motor imagery BCI. IEEE Trans. Fuzzy Syst.
**2017**, 26, 1485–1497. [Google Scholar] [CrossRef] - Postorino, M.N.; Versaci, M. A geometric fuzzy-based approach for airport clustering. Adv. Fuzzy Syst.
**2014**, 2014, 1–12. [Google Scholar] [CrossRef] [Green Version] - Gadekallu, T.R.; Khare, N. Cuckoo search optimized reduction and fuzzy logic classifier for heart disease and diabetes prediction. Int. J. Fuzzy Syst. Appl. (IJFSA)
**2017**, 6, 25–42. [Google Scholar] [CrossRef] - Morabito, E.; Versaci, M. A fuzzy neural approach to localizing holes in conducting plates. IEEE Trans. Magn.
**2001**, 37, 3534–3537. [Google Scholar] [CrossRef] - Burge, R.; Chaparro, A. An investigation of the effect of texting on hazard perception using fuzzy signal detection theory (fSDT). Transp. Res. Part F Traffic Psychol. Behav.
**2018**, 58, 123–132. [Google Scholar] [CrossRef]

**Figure 2.**Example plots from AFIB, AFL, and NSR signal classes. The ECG signal was measured with the aVL lead. The RR intervals, plotted as RR intervals over time, were derived from the ECG via QRS detection. The detrended RR intervals were plotted as RR_DT over time. Visual inspection indicates that the AFIB RR (s) signal shows an additional beat, which has been encircled with a dashed ellipse.

**Figure 3.**ResNet structure used for training and testing: (

**a**) Network super structure; (

**b**) Block structure.

**Table 1.**Data properties for the three signal classes. The ‘ECG Duration (s)’ column provides the time duration of all ECG signal blocks for each individual class. After that, the two columns to the right provide the number of RR intervals and the number of RR_DT samples, respectively. The last two columns on the right provide the number of blocks and number of patients for each signal class.

Property | ECG Duration (s) | RR Intervals | RR_DT Samples | Number of Blocks | Number of Patients | |
---|---|---|---|---|---|---|

Class | ||||||

NSR | 18,260 | 33,976 | 33,976 | 1826 | 1826 | |

AFIB | 17,800 | 25,995 | 25,995 | 1780 | 1780 | |

AFL | 4450 | 7536 | 7536 | 445 | 445 | |

Total | 40,510 | 67,507 | 67,507 | 4051 | 4051 |

**Table 2.**The number of RR intervals per signal class in each part. AFL${}_{\mathrm{SC}}$ denotes the scrambled AFL dataset.

Part | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
---|---|---|---|---|---|---|---|---|---|---|---|

Class | |||||||||||

NSR | 2015 | 1980 | 1980 | 2029 | 2020 | 1973 | 1992 | 2017 | 1975 | 1975 | |

AFIB | 2651 | 2667 | 2584 | 2566 | 2633 | 2649 | 2594 | 2512 | 2604 | 2535 | |

AFL | 742 | 759 | 786 | 784 | 762 | 766 | 727 | 702 | 721 | 787 | |

AFL${}_{\mathrm{SC}}$ | 2226 | 2277 | 2358 | 2352 | 2286 | 2298 | 2181 | 2106 | 2163 | 2361 |

**Table 3.**The number of data vectors per signal class in each part. AFL${}_{\mathrm{P}}$ and AFIB${}_{\mathrm{P}}$ denote the punctured datasets for NSR and AFIB, respectively.

Part | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
---|---|---|---|---|---|---|---|---|---|---|---|

Class | |||||||||||

AFIB | 2651 | 2667 | 2584 | 2566 | 2633 | 2649 | 2594 | 2512 | 2604 | 2535 | |

AFL${}_{\mathrm{SC}}$ | 2226 | 2277 | 2358 | 2352 | 2286 | 2298 | 2181 | 2106 | 2163 | 2361 | |

NSR | 2015 | 1980 | 1980 | 2029 | 2020 | 1973 | 1992 | 2017 | 1975 | 1975 | |

AFL${}_{\mathrm{P}}$ | 2015 | 1980 | 1980 | 2029 | 2020 | 1973 | 1992 | 2017 | 1975 | 1975 | |

AFIB${}_{\mathrm{P}}$ | 2015 | 1980 | 1980 | 2029 | 2020 | 1973 | 1992 | 2017 | 1975 | 1975 |

Fold | Training Data | Testing Data | ||||||
---|---|---|---|---|---|---|---|---|

NSR | AFIB | AFL | Total | NSR | AFIB | AFL | Total | |

1 | 17,941 | 17,941 | 17,941 | 53,823 | 2015 | 2651 | 2226 | 6892 |

2 | 17,976 | 17,976 | 17,976 | 53,928 | 1980 | 2667 | 2277 | 6924 |

3 | 17,976 | 17,976 | 17,976 | 53,928 | 1980 | 2584 | 2358 | 6922 |

4 | 17,927 | 17,927 | 17,927 | 53,781 | 2029 | 2566 | 2352 | 6947 |

5 | 17,936 | 17,936 | 17,936 | 53,808 | 2020 | 2633 | 2286 | 6939 |

6 | 17,983 | 17,983 | 17,983 | 53,949 | 1973 | 2649 | 2298 | 6920 |

7 | 17,964 | 17,964 | 17,964 | 53,892 | 1992 | 2594 | 2181 | 6767 |

8 | 17,939 | 17,939 | 17,939 | 53,817 | 2017 | 2512 | 2106 | 6635 |

9 | 17,981 | 17,981 | 17,981 | 53,943 | 1975 | 2604 | 2163 | 6742 |

10 | 17,981 | 17,981 | 17,981 | 53,943 | 1975 | 2535 | 2361 | 6871 |

Predicted Label | ||||
---|---|---|---|---|

AFIB | AFL | NSR | ||

AFIB | ${N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}$ | ${N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}$ | ${N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}$ | |

True Label | AFL | ${N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}$ | ${N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}$ | ${N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}$ |

NSR | ${N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}$ | ${N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}$ | ${N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}$ |

Predicted Label | |||
---|---|---|---|

Arrhythmia | Non-Arrhythmia | ||

Arrhythmia | ${N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}+{N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}$ | ${N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}$ | |

True Label | $+{N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}+{N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}$ | $+{N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}$ | |

Non-Arrhythmia | ${N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}+{N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}$ | ${N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}$ |

**Table 7.**The average cross-validation confusion matrix. $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}$ indicates the sum over all Test Folds.

Predicted Label | ||||
---|---|---|---|---|

AFIB | AFL | NSR | ||

AFIB | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}\right\$ | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}\right\$ | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}\right\$ | |

True Label | AFL | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}\right\$ | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}\right\$ | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}\right\$ |

NSR | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}\right\$ | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}\right\$ | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}\right\$ |

Fold | $\mathit{cl}$ | ACC${}_{\mathit{cl}}$ (%) | SEN${}_{\mathit{cl}}$ (%) | SPE${}_{\mathit{cl}}$ (%) | Confusion Matrix | ||
---|---|---|---|---|---|---|---|

AFIB | 97.16 | 92.72 | 99.27 | 2064 | 162 | 0 | |

1 | AFL | 97.16 | 98.72 | 96.18 | 34 | 2617 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 2015 | |

AFIB | 99.87 | 99.60 | 100.00 | 2268 | 9 | 0 | |

2 | AFL | 99.87 | 100.00 | 99.79 | 0 | 2667 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 1980 | |

AFIB | 95.81 | 87.70 | 100.00 | 2068 | 290 | 0 | |

3 | AFL | 95.81 | 100.00 | 93.31 | 0 | 2584 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 1980 | |

AFIB | 96.95 | 91.11 | 99.93 | 2143 | 209 | 0 | |

4 | AFL | 96.95 | 99.88 | 95.23 | 3 | 2563 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 2029 | |

AFIB | 98.83 | 96.98 | 99.74 | 2217 | 69 | 0 | |

5 | AFL | 98.83 | 99.54 | 98.40 | 12 | 2621 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 2020 | |

AFIB | 100.00 | 100.00 | 100.00 | 2298 | 0 | 0 | |

6 | AFL | 99.96 | 100.00 | 99.93 | 0 | 2649 | 0 |

NSR | 99.96 | 99.85 | 100.00 | 0 | 3 | 1970 | |

AFIB | 96.81 | 90.10 | 100.00 | 1965 | 216 | 0 | |

7 | AFL | 96.81 | 100.00 | 94.82 | 0 | 2594 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 1992 | |

AFIB | 94.32 | 83.05 | 99.56 | 1749 | 357 | 0 | |

8 | AFL | 94.32 | 99.20 | 91.34 | 20 | 2492 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 2017 | |

AFIB | 98.28 | 95.42 | 99.63 | 2064 | 99 | 0 | |

9 | AFL | 98.15 | 99.35 | 97.39 | 17 | 2587 | 0 |

NSR | 99.86 | 99.54 | 100.00 | 0 | 9 | 1966 | |

AFIB | 100.00 | 100.00 | 100.00 | 2361 | 0 | 0 | |

10 | AFL | 100.00 | 100.00 | 100.00 | 0 | 2535 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 1975 | |

AFIB | 97.82 | 93.76 | 99.81 | 21,197 | 1411 | 0 | |

All | AFL | 97.80 | 99.67 | 96.66 | 86 | 25,909 | 0 |

NSR | 99.98 | 99.94 | 100.00 | 0 | 12 | 19,944 |

ACC${}_{\mathit{cl}}$ (%) | SEN${}_{\mathit{cl}}$ (%) | SPE${}_{\mathit{cl}}$ (%) | Confusion Matrix | |
---|---|---|---|---|

99.98 | 99.94 | 100.00 | 48,603 | 0 |

12 | 19,944 |

**Table 10.**Selected arrhythmia detection studies using RR intervals and ECG. pDB used were: MIT-BIH Atrial Fibrillation Database (afdb), MIT-BIH Arrhythmia Database (mitdb), MIT-BIH Malignant Ventricular Arrhythmia Database (vfdb), Creighton University Ventricular Tachyarrhythmia Database (cudb), MIT-BIH Normal Sinus Rhythm Database (nsrdb), MIT-BIH Long Term Database (ltdb), European ST-T Database (edb), and ecgdb. Hospital data come from non-publicly accessible databases.

Author Year | Method | Data | Performance | ||||
---|---|---|---|---|---|---|---|

Type | DB | Rhythm | ACC | SPE | SEN | ||

Current | Detrending, ResNet | RR | ecgdb | AFIB AFL NSR | 99.98 | 100.00 | 99.94 |

Faust and Acharya 2021 [30] | Detrending, ResNet | RR | ecgdb | SVT, ST, SB, AFIB, AFL, NSR | 98.55 | 94.30 | 99.40 |

Ivanovic et al. 2019 [29] | CNN, LSTM | RR | Hospital | NSR, AFIB AFL | 88 | 87.09 | |

Fujita et al. 2019 [31] | CNN with normalization | ECG | afdb, mitdb, vfdb | AFIB, AFL, VFIB, NSR | 98.45 | 99.87 | 99.27 |

Faust et al. 2018 [32] | LSTM | RR | afdb | AFIB NSR | 98.39 | 98.32 | 98.51 |

Acharya et al. 2017 [33] | CNN with Z-score | ECG | afdb, mitdb, vfdb | AFIB, AFL, VFIB, NSR | 92.50 | 98.09 | 93.13 |

Henzel et al. 2017 [34] | Statistical features with generalized Linear Model | RR | afdb | AFIB NSR | 93 | 95 | 90 |

Desai et al. 2016 [35] | RQA with DecisionTree, RandomForest, RotationForest | ECG | afdb, mitdb, vfdb | AFIB, AFL, VFIB, NSR | 98.37 | ||

Acharya et al. 2016 [36] | Thirteen nonlinear features with ANOVA with KNN and DT | ECG | afdb, mitdb, vfdb | AFIB, AFL, VFIB, NSR | 97.78 | 99.76 | 98.82 |

Hamed et al. 2016 [37] | DWT, PCA and SVM | ECG | afdb | AFIB, AFL, NSR | 98.43 | 96.89 | 98.96 |

Xia et al. 2018 [38] | STFT/SWT with CNN | ECG | afdb | AFIB | 98.63 | 98.79 | 97.87 |

Petrenas et al. 2015 [39] | Median filter with threshold | RR | nsrdb, afdb | AFIB NSR | 98.3 | 97.1 | |

Zhou et al. 2014 [40] | Median filter & Shannon entropy with threshold | RR | ltafdb, afdb, nsrdb | AFIB NSR | 96.05 | 95.07 | 96.72 |

Muthuchudar et al. 2013 [41] | UWT NN | ECG | afdb | AFIB, VFIB, NSR | 96 | ||

Yuan et al. 2016 [42] | Unsupervised autoencoder NN Softmax regression | ECG | afdb, nsrdb, ltdb, hospital | AFIB | 98.18 | 98.22 | 98.11 |

Dinakarrao et al. 2018 [43] | Daubechies-6 with counters Anomaly detector | ECG | mitdb | AFIB, VFIB | 99.19 | 98.25 | 78.70 |

Salem et al. 2018 [44] | Spectogram with CNN | ECG | afdb nsrdb vfdb edb | AFIB, AFL VFIB NSR | 97.23 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Faust, O.; Kareem, M.; Ali, A.; Ciaccio, E.J.; Acharya, U.R.
Automated Arrhythmia Detection Based on RR Intervals. *Diagnostics* **2021**, *11*, 1446.
https://doi.org/10.3390/diagnostics11081446

**AMA Style**

Faust O, Kareem M, Ali A, Ciaccio EJ, Acharya UR.
Automated Arrhythmia Detection Based on RR Intervals. *Diagnostics*. 2021; 11(8):1446.
https://doi.org/10.3390/diagnostics11081446

**Chicago/Turabian Style**

Faust, Oliver, Murtadha Kareem, Ali Ali, Edward J. Ciaccio, and U. Rajendra Acharya.
2021. "Automated Arrhythmia Detection Based on RR Intervals" *Diagnostics* 11, no. 8: 1446.
https://doi.org/10.3390/diagnostics11081446