# Automated Detection of Hypertension Using Physiological Signals: A Review

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. ECG Signals and Blood Pressure (BP) Measurements

#### 1.2. HRV Signal

#### 1.3. Photoplethysmography (PPG Signal)

#### 1.4. Ballistocardiogram (BCG) Signal

## 2. Methods and Material Used in Article Searching

## 3. Databases

#### 3.1. ECG Signal Database

#### 3.2. ECG Derived HRV Signal Databases

#### 3.3. BCG-Derived HRV Signals Database

#### 3.4. PPG Signal Database

## 4. Pre-Processing of ECG Signals

#### 4.1. Normalization

#### 4.2. Segmentation

#### 4.3. Signal Filtering

#### 4.4. Re-Sampling

#### 4.5. Discrete Wavelet Transform (DWT)

#### 4.6. Continuous Wavelet Transform (CWT) Used for PPG Signal Transformation

## 5. Features Extracted in the Review Studies

#### 5.1. HRV Features

#### 5.1.1. HRV Time-Domain Parameters

#### 5.1.2. HRV Frequency-Domain Parameters

#### 5.2. Features of BCG Fluctuation

#### 5.3. Non-Linear Features Extracted from ECG and HRV Signal

#### 5.4. Feature Selection, Reduction, and Ranking

## 6. Computer-Aided Diagnosis Methods

#### Hypertension Diagnosis Index (HDI) [8]

## 7. Proposed Work after Understanding Review Studies

#### 7.1. Features Extracted to the Proposed Work

#### 7.1.1. Sample Entropy (SeEn)

#### 7.1.2. Approximate Entropy (ApEn)

#### 7.1.3. Renyi Entropy (ReEn)

#### 7.1.4. Wavelet Entropy (WlEn)

#### 7.1.5. Log Energy (LOGE)

#### 7.1.6. Signal Fractal Dimension (SLFD)

#### 7.1.7. Hurst Exponent (HE)

#### 7.1.8. Largest Lyapunov Exponent (LLE)

#### 7.1.9. HOS Bispectrum (HOSB)

- (a)
- (b)
- Normalized bispectral square entropy (NBSE) [58]:$$NBSE=-\sum _{n}{q}_{n}log{q}_{n},$$
- (c)
- A weighted center feature of bispectrum (WCOB) is described as [58]:$$WCO{B}_{1m}=\frac{{\sum}_{\mathsf{\Omega}\left(kB\right(k,l\left)\right)}}{{\sum}_{\mathsf{\Omega}B(k,l)}}$$$$WCO{B}_{2m}=\frac{{\sum}_{\mathsf{\Omega}\left(lB\right(k,l\left)\right)}}{{\sum}_{\mathsf{\Omega}B(k,l)}}.$$Here, k and l represents the frequency bin index in the principle region of bispectrum plot [58]. Similarly, some moments related features are given below:
- (d)
- Bispectrum logarithmic amplitude feature [58]:$${M}_{1}=\sum _{\mathsf{\Omega}}log\left(\right|B({f}_{1},{f}_{2})\left|\right).$$
- (e)
- Bispectrum sum of logarithmic amplitude of diagonal elements feature [58]:$${M}_{2}=\sum _{\mathsf{\Omega}}log\left(\right|B({f}_{q},{f}_{q})\left|\right).$$
- (f)
- Bispectrum first-order spectral moments of amplitude of diagonal elements feature [58]:$${M}_{3}=\sum _{q=1}^{N}klog\left(\right|B({f}_{q},{f}_{q})\left|\right).$$
- (g)
- Bispectrum mean magnitude feature [58]:$$mAmp=\frac{1}{L}\sum _{\mathsf{\Omega}}\left|b({f}_{1},{f}_{2})\right|.$$
- (h)
- Bispectrum phase entropy feature [58] :$$P{h}_{e}=\sum _{n}p\left({\mathsf{\Phi}}_{n}\right)logp\left({\mathsf{\Phi}}_{n}\right).$$

#### 7.1.10. Higher Order Spectral Cumulant (HOSC)

#### 7.1.11. Recurrence Plot (RP)

#### 7.1.12. Recurrence Quantification Analysis (RQA)

#### 7.2. Results

## 8. Discussion

- Rajput et al. [8] developed an HDI accurately using ECG signals to stratify low-risk versus high-risk HT with a single numeric value.
- Poddar et al. [28] used HRV signals to classify HT and normal subjects using SVM classifier with 100% accuracy using 20 features. They have used a balanced data set of 56 normal and 57 HT subjects in their study.
- Rajput et al. [23] classified ECG signals into three classes (LRHT, HRHT, and HC) using features extracted from the five-level wavelet decomposition of ECG signals. They have obtained 99.95% classification accuracy using SeEn and WeEn features with unbalanced data set. Testing error is found to be only 3.26% with hold-out validation method.
- Soh et al. [24] developed a CNN architecture for the classification of normal and HT ECG classes and achieved an accuracy of 99.99%, sensitivity of 100% and specificity of 99.97%.

## 9. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Unger, T.; Borghi, C.; Charchar, F.; Khan, N.; Poulter, N.; Dorairaj, P.; Ramirez, A.; Schlaich, M.; Stergiou, G.; Tomaszewski, M.; et al. 2020 International Society of Hypertension Global Hypertension Practice Guidelines. Hypertension
**2020**, 75. [Google Scholar] [CrossRef] [PubMed] - Soh, D.; Ng, E.; Vicnesh, J.; Oh, S.L.; Tan, R.S.; Acharya, U.R. A computational intelligence tool for the detection of hypertension using empirical mode decomposition. Comput. Biol. Med.
**2020**, 118, 103630. [Google Scholar] [CrossRef] [PubMed] - Da S. Luz, E.J.; Schwartz, W.R.; Camara-Chavez, G.; Menotti, D. ECG-based heartbeat classification for arrhythmia detection: A survey. Comput. Methods Programs Biomed.
**2016**, 127, 144–164. [Google Scholar] [CrossRef] - Estrada, G.; Luis, M.; Mendoza, l.e.; Sc, M.; Molina, V. Relationship of blood pressure with the electrical signal of the heart using signal processing. Tecciencia
**2014**. [Google Scholar] [CrossRef][Green Version] - Sharma, M.; Dhiman, H.S.; Acharya, U.R. Automatic identification of insomnia using optimal antisymmetric biorthogonal wavelet filter bank with ECG signals. Comput. Biol. Med.
**2021**, 104246. [Google Scholar] [CrossRef] - Sharma, M.; Tan, R.S.; Acharya, U.R. Automated heartbeat classification and detection of arrhythmia using optimal orthogonal wavelet filters. Inform. Med. Unlocked
**2019**, 100221. [Google Scholar] [CrossRef] - Simjanoska, M.; Gjoreski, M.; Madevska Bogdanova, A.; Koteska, B.; Gams, M.; Tasic, J. ECG-derived Blood Pressure Classification using Complexity Analysis-based Machine Learning. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies—HEALTHINF, Madeira, Portugal, 19-21 January 2018; pp. 282–292. [Google Scholar] [CrossRef]
- Rajput, J.S.; Sharma, M.; Acharya, U.R. Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank. Int. J. Environ. Res. Public Health
**2019**, 16, 4068. [Google Scholar] [CrossRef][Green Version] - Sharma, M.; Singh, S.; Kumar, A.; Tan, R.S.; Acharya, U.R. Automated detection of shockable and non-shockable arrhythmia using novel wavelet-based ECG features. Comput. Biol. Med.
**2019**, 103446. [Google Scholar] [CrossRef] - Sharma, M.; Acharya, U.R. A new method to identify coronary artery disease with ECG signals and time-Frequency concentrated antisymmetric biorthogonal wavelet filter bank. Pattern Recognit. Lett.
**2019**, 125, 235–240. [Google Scholar] [CrossRef] - Sharma, M.; Tan, R.S.; Acharya, U.R. A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank. Comput. Biol. Med.
**2018**. [Google Scholar] [CrossRef] - Sharma, M.; Agarwal, S.; Acharya, U.R. Application of an optimal class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ECG signals. Comput. Biol. Med.
**2018**, 100, 100–113. [Google Scholar] [CrossRef] - Ni, H.; Wang, Y.; Xu, G.; Shao, Z.; Zhang, W.; Zhou, X. Multiscale Fine-Grained Heart Rate Variability Analysis for Recognizing the Severity of Hypertension. Comput. Math. Methods Med.
**2019**, 2019, 1–9. [Google Scholar] [CrossRef][Green Version] - Acharya, U.R.; Ae, A.; Paul, K.; Ae, J.; Kannathal, N.; Choo, A.; Lim, M.; Jasjit, A.; Suri, S. Heart rate variability: A review. Med. Biol. Eng. Comput.
**2014**, 44, 1031–1051. [Google Scholar] [CrossRef] - Lan, K.C.; Raknim, P.; Kao, W.F.; Huang, J.H. Toward Hypertension Prediction Based on PPG-Derived HRV Signals: A Feasibility Study. J. Med Syst.
**2018**, 42. [Google Scholar] [CrossRef] - Melillo, P.; Izzo, R.; Orrico, A.; Scala, P.; Attanasio, M.; Mirra, M.; Luca, N.; Pecchia, L. Automatic Prediction of Cardiovascular and Cerebrovascular Events Using HRV Analysis. PLoS ONE
**2015**, 10, e0118504. [Google Scholar] [CrossRef] - Malik, M. Chapter 89 - Heart Rate Variability and Baroreflex Sensitivity. In Cardiac Electrophysiology, 4th ed.; Zipes, D.P., Jalife, J., Eds.; W.B. Saunders Elsevier: Amsterdam, The Netherlands, 2004; pp. 823–830. [Google Scholar] [CrossRef]
- Natarajan, N.; Balakrishnan, A.; Ukkirapandian, K. A study on analysis of Heart Rate Variability in hypertensive individuals. Int. J. Biomed. Adv. Res.
**2014**, 5, 109. [Google Scholar] [CrossRef][Green Version] - Elgendi, M.; Fletcher, R.; Liang, Y.; Howard, N.; Lovell, N.; Abbott, D.; Lim, K.; Ward, R. The use of photoplethysmography for assessing hypertension. Nat. Med.
**2019**, 2. [Google Scholar] [CrossRef][Green Version] - Liang, Y.; Chen, Z.; Ward, R.; Elgendi, M. Hypertension Assessment Using Photoplethysmography: A Risk Stratification Approach. J. Clin. Med.
**2018**, 8, 12. [Google Scholar] [CrossRef][Green Version] - Liu, F.; Zhou, X.; Wang, Z.; Cao, J.; Wang, H.; Zhang, Y. Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining. Sensors
**2019**, 19, 1489. [Google Scholar] [CrossRef][Green Version] - Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.; Antes, G.; Atkins, D.; Barbour, V.; Barrowman, N.; Berlin, J.; Clark, J.; et al. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Rev. Esp. De Nutr. Humana Y Diet.
**2014**, 18, 172–181. [Google Scholar] [CrossRef] - Rajput, J.S.; Sharma, M.; Tan, R.S.; Acharya, U.R. Automated detection of severity of hypertension ECG signals using an optimal bi-orthogonal wavelet filter bank. Comput. Biol. Med.
**2020**, 103924. [Google Scholar] [CrossRef] [PubMed] - Soh, D.; Ng, E.; Vicnesh, J.; Oh, S.L.; Tan, R.S.; Acharya, U.R. Automated diagnostic tool for hypertension using convolutional neural network. Comput. Biol. Med.
**2020**. [Google Scholar] [CrossRef] [PubMed] - Jain, P.; Gajbhiye, P.; Tripathy, R.; Acharya, U.R. A two-stage Deep CNN Architecture for the Classification of Low-risk and High-risk Hypertension Classes using Multi-lead ECG Signals. Inform. Med. Unlocked
**2020**. [Google Scholar] [CrossRef] - Alkhodari, M.; Islayem, D.; Alskafi, F.; Khandoker, A. Predicting hypertensive patients with higher risk of developing vascular events using heart rate variability and machine learning. IEEE Access
**2020**. [Google Scholar] [CrossRef] - Tejera, E.; Areias, M.; Rodrigues, A.; Ramõa, A.; Nieto-villar, J.; Rebelo, I. Artificial neural network for normal, hypertensive, and preeclamptic pregnancy classification using maternal heart rate variability indexes. J. Matern.-Fetal Neonatal Med. Off. J. Eur. Assoc. Perinat. Med. Fed. Asia Ocean. Perinat. Soc. Int. Soc. Perinat. Obstet.
**2011**, 24, 1147–1151. [Google Scholar] [CrossRef] - Poddar, M.; Kumar, V.; Sharma, Y.P. HRV based Classification of Normal and Hypertension Cases by Linear-nonlinear Method. Def. Sci. J.
**2014**, 64, 542–548. [Google Scholar] [CrossRef] - Poddar, M.; Birajdar, A.; Virmani, J.K. Automated Classification of Hypertension and Coronary Artery Disease Patients by PNN, KNN, and SVM Classifiers Using HRV Analysis. In Machine Learning in Bio-Signal Analysis and Diagnostic Imaging; Academic Press: Amsterdam, The Netherlands, 2019; pp. 99–125. [Google Scholar] [CrossRef]
- Ni, H.; Cho, S.; Mankoff, J.; Yang, J.; Dey, A. Automated recognition of hypertension through overnight continuous HRV monitoring. J. Ambient Intell. Humaniz. Comput.
**2017**, 9. [Google Scholar] [CrossRef] - Kublanov, V.; Dolganov, A.; Belo, D.; Gamboa, H. Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics. Appl. Bionics Biomech.
**2017**, 2017, 1–13. [Google Scholar] [CrossRef][Green Version] - Mussalo, H.; Vanninen, E.; Ikäheimo, R.; Laitinen, T.; Laakso, M.; Länsimies, E.; Hartikainen, J. Heart rate variability and its determinants in patients with severe or mild essential hypertension. Clin. Physiol.
**2008**, 21, 594–604. [Google Scholar] [CrossRef] - Song, Y.; Ni, H.; Zhou, X.; Zhao, W.; Wang, T. Extracting Features for Cardiovascular Disease Classification Based on Ballistocardiography. In 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom); IEEE: Beijing, China, 2015; pp. 1230–1235. [Google Scholar] [CrossRef]
- Liang, Y.; Chen, Z.; Ward, R.; Elgendi, M. Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification. Biosensors
**2018**, 8, 101. [Google Scholar] [CrossRef][Green Version] - Liang, Y.; Chen, Z.; Ward, R.; Elgendi, M. Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database. Diagnostics
**2018**, 8, 65. [Google Scholar] [CrossRef][Green Version] - Ghosh, A.; Mayor Torres, J.; Danieli, M.; Riccardi, G. Detection of Essential Hypertension with Physiological Signals from Wearable Devices. In Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, 25–29 August 2015. [Google Scholar] [CrossRef]
- Poddar, M.; Kumar, V.; Sharma, Y. Linear-nonlinear heart rate variability analysis and SVM based classification of normal and hypertensive subjects. J. Electrocardiol.
**2013**, 46, e25. [Google Scholar] [CrossRef] - Koichubekov, B.; Sorokina, M.; Laryushina, Y.; Luydmila, T.; Korshukov, I. Nonlinear analyses of heart rate variability in hypertension. Ann. De Cardiol. Et D’Angéiologie
**2018**, 67. [Google Scholar] [CrossRef] - Satija, U.; Ramkumar, B.; Manikandan, M. Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring. IEEE J. Biomed. Health Informatics
**2017**, PP. [Google Scholar] [CrossRef] - Sharma, M.; Patel, V.; Acharya, U.R. Automated identification of insomnia using optimal bi-orthogonal wavelet transform technique with single-channel EEG signals. Knowl. Based Syst.
**2021**, 107078. [Google Scholar] [CrossRef] - Sharma, M.; Patel, S.; Acharya, U.R. Automated detection of abnormal EEG signals using localized wavelet filter banks. Pattern Recognit. Lett.
**2020**, 133, 188–194. [Google Scholar] [CrossRef] - Sharma, M.; Tan, R.S.; Acharya, U.R. Detection of shockable ventricular arrhythmia using optimal orthogonal wavelet filters. Neural Comput. Appl.
**2019**. [Google Scholar] [CrossRef] - Sharma, M.; Acharya, U.R. Automated detection of schizophrenia using optimal wavelet-based l
_{1}norm features extracted from single-channel EEG. Cogn. Neurodynamics**2021**, 1–14. [Google Scholar] [CrossRef] - Sharma, M.; Patel, S.; Choudhary, S.; Acharya, U.R. Automated Detection of Sleep Stages Using Energy-Localized Orthogonal Wavelet Filter Banks. Arab. J. Sci. Eng.
**2019**. [Google Scholar] [CrossRef] - Sharma, M.; Acharya, U.R. Analysis of knee-joint vibroarthographic signals using bandwidth-duration localized three-channel filter bank. Comput. Electr. Eng.
**2018**, 72, 191–202. [Google Scholar] [CrossRef] - Zala, J.; Sharma, M.; Bhalerao, R. Tunable Q - wavelet transform based features for automated screening of knee-joint vibroarthrographic signals. In Proceedings of the 2018 International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 22–23 February 2018. [Google Scholar] [CrossRef]
- Sharma, M.; Bhurane, A.A.; Acharya, U.R. MMSFL-OWFB: A novel class of orthogonal wavelet filters for epileptic seizure detection. Knowl.-Based Syst.
**2018**, 160, 265–277. [Google Scholar] [CrossRef] - Kamath, M.; Watanabe, M.; Upton, A. Heart Rate Variability (HRV) Signal Analysis: Clinical Applications; CRC Press: Boca Raton, FL, USA, 2016; pp. 1–502. [Google Scholar]
- Garcia Martinez, C.; Otero, A.; Vila, X.; Tourino, M.; Rodriguez-Linares, L.; Presedo, J.; Mendez, A. Heart Rate Variability Analysis with the R Package RHRV; Springer: Cham, Switzerland, 2017. [Google Scholar] [CrossRef]
- Renyi, A. On measures of entropy and information. Proc. 4th Berkeley Symp. Math. Stat. Prob.
**1961**, 1, 547–561. [Google Scholar] - Sharma, M.; Tiwari, J.; Acharya, U.R. Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals. Int. J. Environ. Res. Public Health
**2021**, 18. [Google Scholar] [CrossRef] - Rosso, O.; Blanco, S.; Yordanova, J.; Figliola, A.; Schürmann, M.; Basar, E. Wavelet entropy: A new tool for analysis of short duration brain electrical signals. J. Neurosci. Methods
**2001**, 105, 65–75. [Google Scholar] [CrossRef] - Sharma, M.; Raval, M.; Acharya, U.R. A new approach to identify obstructive sleep apnea using an optimal orthogonal wavelet filter bank with ECG signals. Informatics Med. Unlocked
**2019**, 16, 100170. [Google Scholar] [CrossRef] - Acharya, U.R.; Subbhuraam, V.S.; Ang, P.; Yanti, R.; Suri, J. Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int. J. Neural Syst.
**2012**, 22, 1250002. [Google Scholar] [CrossRef] - Acharya, U.R.; Fujita, H.; K Sudarshan, V.; Oh, S.L.; Adam, M.; Koh, J.E.W.; Tan, J.H.; Ghista, D.; Martis, R.; Chua, K.; et al. Automated Detection and Localization of Myocardial Infarction Using Electrocardiogram: A Comparative Study of Different Leads. Knowl. Based Syst.
**2016**, 99. [Google Scholar] [CrossRef] - Martis, R.; Acharya, U.R.; Mandana, K.; Ray, A.; Chakraborty, C. Cardiac decision making using higher order spectra. Biomed. Signal Process. Control
**2013**, 8, 193–203. [Google Scholar] [CrossRef] - Acharya, U.R.; Subbhuraam, V.S.; Goutham, S.; Martis, R.; Suri, J. Automated EEG analysis of epilepsy: A review. Knowl. Based Syst.
**2013**, 45, 147–165. [Google Scholar] [CrossRef] - Chua, K.; Chandran, V.; Acharya, U.R.; Lim, C. Application of higher order statistics/spectra in biomedical signals—A review. Med. Eng. Phys.
**2010**, 32, 679–689. [Google Scholar] [CrossRef] [PubMed][Green Version] - Oh, S.L.; Vicnesh, J.; Tan, R.S.; Ciaccio, E.; Yamakawa, T.; Tanabe, M.; Kobayashi, M.; Faust, O.; Acharya, U.R. Comprehensive electrocardiographic diagnosis based on deep learning. Artif. Intell. Med.
**2020**, 103. [Google Scholar] [CrossRef]

**Figure 1.**ECG waveform with standard intervals. Correlations have been found between systolic (SBP) and diastolic blood pressure (DBP) measurements and morphological data in the corresponding indicated epochs. The Figure is generated from PTB database (subject no. 14).

**Figure 4.**Workflow of proposed methods for HT diagnosis using ECG signals. HC represents healthy control, LRHT, low-risk hypertension, and HRHT is high-risk hypertension.

**Table 1.**Classification of HT based on office blood pressure measurement [1].

Category | Systolic (mm Hg) | Diastolic (mm Hg) | |
---|---|---|---|

Normal BP | <130 | and | <85 |

High-normal BP | 130–139 | and/or | 85–89 |

Grade 1 hypertension | 140–149 | and/or | 90–99 |

Grade 2 hypertension | ≥160 | and/or | ≥100 |

S No. | Author/Year | Signal | Feature | Method | Subject | Database | Results |
---|---|---|---|---|---|---|---|

1 | Rajput et al. [2019] [8] | ECG | Signal fractal dimension and Log energy | Wavelet decomposition using FB, feature extraction, student-t test, developed index | 139 | SHAREE | 100 % discrimination of LRHT, HRHT |

2 | Soh et al. [2020] [2] | ECG | 18, non-linear | EMD is used to decomposed ECG signal up-to 5 level using IMF, feature extraction, student t-test and then used supervised KNN classifier | 157 | SHAREE, MIT-BIH | ACC = 97.70% , SEN = 98.90%, SPE = 89.10% |

3 | Rajput et al. [2020] [23] | ECG | SeEn and WlEn | Wavelet decomposition using FB, feature extraction, used EBT classifier to classify severity of HT | 191 | SHAREE, PTB | ACC = 99.95%, SEN = 98.64%, SPE = 99.91%, F1 = 97.3% AUC = 1 |

4 | Liang et al. [2018] [35] | ECG, PPG | Ratio, Slope, Power area, waveform area, VPG and APG, Time span, PPG amplitude, PAT Feature | Classification of HT | 121 | MIMIC | SEN = 94.26%, SPE = 96.17%, F1 = 94.84% |

5 | Soh et al. [2020] [24] | ECG | Total 1507 | Classification using CNN, DL model | 157 | SHAREE, MIT-BIH | ACC = 99.99%, SEN = 100%, SPE = 99.97% |

6 | Jain et al. [2020] [25] | ECG | 11 layer CNN | Classification using CNN, DL model | 191 | SHAREE | ACC = 99.68% |

7 | Present study | ECG | HOS, bispectrum, Cumulant, RQA | Direct feature extraction and classification | 191 | SHAREE, PTB | ACC = 98.05%, SEN = 95.66%, SPE = 96.58% |

S No. | Author/Year | Signal | Feature | Method | Subject | Database | Results |
---|---|---|---|---|---|---|---|

1 | Melillo et al. [2015] [16] | HRV | PP ( SD1 and SD2), CD, DFA (features: Alpha1,Alpha2), and RP and HRV | Statistical analysis | 139 | SHAREE | ACC = 85.7%, SEN = 71.4%, SPE = 87.8% |

2 | Ni et al. [2019] [13] | HRV | 18 HRV multidimensional features | Wavelet transform, | 139 | SHAREE | AUC = 0.95 |

3 | Y.song et al. [2015] [33] | HRV, BCG | HRV time and frequency domain feature and DFA | EEMD, data-mining, DFA | 18 | Private | ACC = 92.3% |

4 | Poddar et al. [2014] [37] | HRV | Nonlinear parameters of PP, ApEn and SeEn and HRV time and frequency domain feature | Classification of HRV | 113 | Private | ACC = 100%, SEN = 100%, SPE = 100% |

5 | Natrajan et al.[2014] [18] | HRV | HRV feature | Statistical analysis using SPSS | 60 | Private | HRV reduce in HT subjects |

6 | Ni et al. [2017] [30] | HRV | ApEn and SeEn and HRV time and frequency domain feature | Classification of HRV signal | 24 | Private | ACC = 93.3% |

7 | Poddar et al. [2019] [29] | HRV | HRV time and frequency domain feature | Classification of HRV | 185 | Private | ACC = 96.7% |

8 | Koichub et al. [2018] [38] | HRV | HRV time and frequency domain feature, CD | Statistical analysis | 56 | Private | HRV decreased in HT group |

9 | Tejera et al. [2011] [27] | HRV | LZ, and SeEn, HRV time and frequency domain feature | ANN | 568 | Private | SPE = 90% , AUC = 0.98 |

10 | Mussalo et al. [2008] [32] | HRV | HRV time and frequency domain feature | Statistical analysis using SPSS | 97 | Private | LF, HF power decrease in SEHT group |

11 | Liu et al. [2019] [21] | HRV, BCG | HRV time and frequency domain feature, SeEn, DFA, BCG fluctuation features | Classification, feature extraction, selection, identification of HT | 128 | Open source | ACC = 84.4%, PRE = 82.5%, REC = 85.3% |

12 | Kublanov et al. [2017] [31] | HRV, ECG | CWT, HRV feature | Classification of HT | 71 | Private | Score = 91.33% ± 1.73 |

13 | Alkhodari et al. [2020] [26] | HRV | HRV feature | Low and high-risk HT | 139 | SHAREE | ACC = 97.08% |

S No. | Author/Year | Signal | Feature | Method | Subject | Database | Results |
---|---|---|---|---|---|---|---|

1 | Liang et al. [2018] [34] | PPG | CWT | Classification using Pre-trained CNN (GoogLeNet, 144 layer) | 121 | MIMIC | F1-score = 92.55% |

2 | Liang et al. [2018] [20] | PPG | Ratio, Slope, Power area, waveform area, VPG and APG, Time span, PPG amplitude | Classification of HT | 124 | Private | PP = 100%, SE = 85.71%, F1-score = 92.31% |

3 | Lan et al. [2018] [15] | PPG, HRV | HRV time and frequency domain feature | Data mining | 43 | Private | ACC = 85.47%, SPE = 83.33%, PRE = 92.11% |

4 | Ghose et al. [36] | PPG , HRV | Mean, SD, min and max, HRV time and frequency domain feature | Classification of HT | 20 | Private | F1-score = 83% |

**Table 5.**Summary of bispectrum features (mean ± standard deviation) values obtained for three classes.

Bispectrum | LRHT | HRHT | HC |
---|---|---|---|

$NB{E}_{1}$ | 0.927 ± 0.044 | 0.866 ± 0.076 | 0.582 ± 0.237 |

$NBSE$ | 0.707 ± 0.161 | 0.538 ± 0.181 | 0.097 ± 0.11 |

$WCO{B}_{1m}$ | 3324 ± 1369 | 2297 ± 1170 | 714 ± 820 |

$WCO{B}_{2m}$ | 1524 ± 733 | 846 ± 620 | 333 ± 387 |

${M}_{1}$ | 1.9 × ${10}^{8}$ ± 1.4×${10}^{7}$ | 1.8 ×${10}^{8}$ ± 1.3 × ${10}^{7}$ | 3×${10}^{8}$ ± 1.4×${10}^{7}$ |

${M}_{2}$ | 40,873 ± 2946 | 38,718 ± 2743 | 63,642 ± 2803 |

${M}_{3}$ | 9.7×${10}^{7}$ ± 7×${10}^{6}$ | 8.9×${10}^{7}$ ± 6×${10}^{6}$ | 1.5×${10}^{8}$ ± 7×${10}^{6}$ |

mAmp | 4.3×${10}^{9}$ ± 4× ${10}^{10}$ | 9.6×${10}^{9}$ ± 3.5× ${10}^{9}$ | 7.7×${10}^{14}$ ± 1.8× ${10}^{15}$ |

$P{h}_{e}$ | 3.58 ± 0.00028 | 3.58 ± 0.00048 | 3.56 ± 0.063 |

RQA | LRHT | HRHT | HC |
---|---|---|---|

RR | 8×${10}^{-4}$ ± 7×${10}^{-5}$ | 9× ${10}^{-4}$ ± 8 ×${10}^{-5}$ | 5× ${10}^{-4}$ ± 1× ${10}^{-5}$ |

DET | 0.375 ± 0.0928 | 0.483 ± 0.138 | 0.508 ± 0.0972 |

ENT | 0.486 ± 0.112 | 0.628 ± 0.190 | 0.662 ± 0.143 |

LMR | 2.448 ± 0.407 | 2.685 ± 0.868 | 2.748 ± 0.289 |

**Table 7.**HOS cumulant second, third, and fourth order features computed (mean ± standard deviation) values obtained for three classes.

HOS Feature | LRHT | HRHT | HC |
---|---|---|---|

Cumulant${}_{2}$ | 125.23 ± 432.91 | 93.42 ± 176.32 | 8×${10}^{5}$ ± 2× ${10}^{6}$ |

Cumulant${}_{3}$ | 17.232 ± 4269.5 | −1111.3 ± 4534 | −8 ×${10}^{8}$ ± 7×${10}^{9}$ |

Cumulant${}_{4}$ | 92,476 ± 1 ×${10}^{6}$ | 1× ${10}^{5}$ ± 5 ×${10}^{5}$ | 3× ${10}^{12}$ ± 6 ×${10}^{13}$ |

HC | HRHT | LRHT | |
---|---|---|---|

HC | 79 | 0 | 1 |

HRHT | 0 | 393 | 49 |

LRHT | 0 | 22 | 3150 |

**Table 9.**Performance parameters obtained using HOS bispectrum, cumulants and RQA features with SVM classifier.

Class | Accuracy% | Sensitivity% | Specificity% | F1-Score% |
---|---|---|---|---|

HC | 99.97 | 98.75 | 100 | 99.37 |

HRHT | 98.07 | 88.87 | 99.32 | 91.71 |

LRHT | 98.05 | 99.30 | 90.42 | 98.87 |

S.No | Feature | Accuracy % | AUC | Classifier |
---|---|---|---|---|

1 | HOS cumulant order2,3,4 | 90.2 | 0.99 | EBT |

2 | HOS bispectrum | 96.3 | 0.99 | KNN |

3 | RQA | 91.0 | 1.00 | EBT |

4 | bispectrum, Cumulant, RQA | 98.05 | 1.00 | SVM |

4 | SeEn | 84.3 | 0.74 | TREE |

5 | WeEn | 88.0 | 0.96 | TREE |

6 | ApEn | 81.8 | 0.94 | EBT |

7 | ReEn | 78.9 | 0.88 | EBT |

8 | SeEn, WeEn, ApEN, ReEn | 89.1 | 0.97 | EBT |

9 | SLFD | 87.1 | 0.96 | SVM |

10 | HE | 87.8 | 0.92 | SVM |

11 | LLE | 82.4 | 0.86 | NB |

13 | SLFD, HE, LLE, | 88.1 | 0.97 | EBT |

15 | LOGE | 86.4 | 0.94 | TREE |

16 | SeEn, WeEn, ApEN, ReEn, SLFD, HE, LLE, LOGE | 95.5 | 0.99 | EBT |

S No. | Author/Year | Type of ML | Classifier |
---|---|---|---|

1 | Soh et al. [2020] [2] | Supervised ML | KNN |

2 | Melillo et al. [2015] [16] | Supervised ML | AB, NB, RF, SVM |

3 | Ni et al. [2019] [13] | Supervised ML | SVM,RF,NB |

4 | Song et al. [2015] [33] | Supervised ML | SVM, RF, KNN |

5 | Poddar et al. [2014] [37] | Supervised ML | SVM |

6 | Ni et al. [2017] [30] | Supervised ML | Linear SVM |

7 | Poddar et al. [2019] [29] | Supervised ML | SVM, KNN |

8 | Tejera et al. [2011] [27] | ANN | ANN |

9 | Rajput et al. [2020] [23] | Supervised ML | KNN, SVM, TREE, and EBT |

10 | Liu et al. [2019] [21] | Supervised ML | SVM, DT, NB |

11 | Liang et al. [2018] [34] | DL | CNN, GoogLeNet |

12 | Liang et al. [2018] [20] | Supervised ML | LDA, SVM, KNN, LR |

13 | Liang et al. [2018] [35] | Supervised ML | AB, KNN, EBT, LR |

14 | Lan et al. [2018] [15] | Semi-supervised learning | - |

15 | Ghose et al. [36] | Supervised ML | AB, KNN, EBT, DT, RF, NB, SVM |

16 | Kublanov et al. [31] | Supervised ML | LDA, SVM, KNN, NB, DT |

17 | Soh et al. [2020] [24] | DL model | CNN |

18 | Jain et al. [2020] [25] | DL model | |

19 | Alkhodari et al. [2020] [26] | ML | RUSBOOST, TREE, SVM |

20 | Present study | Supervised ML | KNN, EBT, SVM |

Abbreviation | Full Form | Abbreviation | Full Form |
---|---|---|---|

SLFD | Signal fractal dimensions | LOGE | Log energy |

LLE | Largest Lyapunov Exponent | HOS | Higher order spectral |

OGWB | Orthogonal wavelet filter bank | ||

HT | Hypertension | SBP | Systolic blood pressure |

HRV | Heart rate variability | DBP | Diastolic blood pressure |

ECG | Electrocardiography | DWT | Discrete Wavelet Transform |

PPG | Photoplethysmography | BCG | Ballistocardiogram |

LVH | left ventricular hypertrophy | VG | ventricular gradient |

PPG | Photoplethysmography | HDI | Hypertension diagnosis index |

ML | Machine learning | ||

DL | Deep Learning | ANN | Artificial Neural Network |

CNN | Convolution neural network | RNN | Recurrent Nural Network |

SVM | Support vector machine | KNN | K-nearest neighbour |

CWT | Continuous Wavelet Transform | FFT | Fast Fourier transform |

ANOVA | Analysis of variance | ROC | Receiver operating characteristics |

EBT | Ensemble Bagged Tree | AB | Ada boost |

LR | Logistic Regression | NB | Navy Bayes |

RF | Random Forrest | LRA | Linear Regression Analysis |

SeEn | Sample entropy | ApEn | Approximate entropy |

ReEn | Reny entropy | WlEn | Wavelet entropy |

DFA | Detrended fluctuation analysis | CD | Correlation Dimension |

LZ | Lempel-Ziv complexity | RC | Recurrence Plot |

PP | Poincare plot | EMD | Empirical Mode Decomposition |

VPG | Velocity plethysmogram | APG | Acceleration plethysmogram |

PAT | Pulse arrival time | INVD | Inverse dower |

ACC | Accuracy | SPE | Specificity |

SEN | Sensitivity | PRE | Precision |

REC | Recall | AUC | Area under the curve |

PPV | Positive predictive value | NPV | Negative Predictive Value |

SPSS | Statistical Package for the Social Sciences | MANOVA | Multivariate analysis of variance MANOVA |

PRISMA | Preferred reporting items for systematic reviews and meta-analyses | HRHT | High-risk hypertension |

RUSBOOST | random under-sampling boosting | KNN | K-nearest neighbour |

HC | Healthy control | LRHT | Low-risk hypertension |

DT | Decision tree | LDA | Linear Discriminate analysis |

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**

Sharma, M.; Rajput, J.S.; Tan, R.S.; Acharya, U.R. Automated Detection of Hypertension Using Physiological Signals: A Review. *Int. J. Environ. Res. Public Health* **2021**, *18*, 5838.
https://doi.org/10.3390/ijerph18115838

**AMA Style**

Sharma M, Rajput JS, Tan RS, Acharya UR. Automated Detection of Hypertension Using Physiological Signals: A Review. *International Journal of Environmental Research and Public Health*. 2021; 18(11):5838.
https://doi.org/10.3390/ijerph18115838

**Chicago/Turabian Style**

Sharma, Manish, Jaypal Singh Rajput, Ru San Tan, and U. Rajendra Acharya. 2021. "Automated Detection of Hypertension Using Physiological Signals: A Review" *International Journal of Environmental Research and Public Health* 18, no. 11: 5838.
https://doi.org/10.3390/ijerph18115838