# Selective Electrochemical Detection of SARS-CoV-2 Using Deep Learning

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. UFC-19 Sensor

^{TM}, Fair Lawn, NJ, USA, Lot# A0411825) to a total volume 2.0 mL. More details about the method can be found in the literature. The working electrode was rotated during testing at 400 rpm to flow the electrolyte containing SARS-CoV-2 towards the electrode surface [17,18,22]. UFC-19 senses the presence of SARS-CoV-2 electrochemically by producing a current response when an electric potential is applied. When SARS-CoV-2 is present in a sample, a positive current spike compared to a baseline/background (devoid of SARS-CoV-2) current is produced at a short response time ensuring that the SARS-CoV-2 spike protein in the sample has been sensed. It is hypothesized that the positively charged hydrogen occupancies on the SARS-CoV-2 S1 spike protein interacts with the negatively charged electrocatalyst upon the application of voltage resulting in electrostatic charges recorded as current due to electron flow [17].

#### 2.2. Initial Signature Analysis of SARS-CoV-2 and Comparison with Other Viruses

#### 2.3. Machine Learning and Deep Learning Algorithms

#### 2.3.1. Machine Learning Algorithms with Manual Feature Extraction

#### 2.3.2. Convolution Neural Networks (CNN)

## 3. Results and Discussion

#### 3.1. Results of Initial Signature Analysis of SARS-CoV-2 and Comparison with Other Viruses

#### 3.2. Machine Learning and Deep Learning Results

#### 3.2.1. Machine Learning

#### 3.2.2. Deep Learning Results

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Example current response of a SARS-CoV-2 positive sample with 0.1 cp/mL concentration, baseline (true negative), and sample–baseline difference. The SARS-CoV-2 sample current response is higher than the baseline sample.

**Figure 2.**Univariate feature scores of the futures. The bar chart shows the informative and non-informative features. The most informative features are F0, F6, and F3 and the least informative features are F5, F11, and F14.

**Figure 3.**(

**a**) Signature analysis of 400 SARS-CoV-2 positive samples. All current response difference points for 400 samples are above the threshold line. Meaning all samples were diagnosed correctly as SARS-CoV-2 positive. (

**b**) Signature analysis of 400 blank samples. All current response difference points for 400 samples are below the threshold line. Meaning all samples were diagnosed correctly as SARS-CoV-2 negative.

**Figure 4.**(

**a**) Signature analysis of 400 SARS-CoV samples. The 242 out of 400 samples were misclassified as SARS-CoV-2 positive and the rest of the samples falls below the threshold line (

**b**) Signature analysis of 400 HCoV-OC43 samples. Most of the samples were correctly classified as SARS-CoV-2 negative. Only 9 samples were classified as SARS-CoV-2 positive.

**Figure 5.**(

**a**) Signature analysis of 400 MERS-CoV samples, while 356 samples were above the threshold and classified as SARS-CoV-2, 44 samples were under the threshold. (

**b**) Signature analysis of 400 Influenza virus samples, only 14 samples are identified as SARS-CoV-2 positive.

**Figure 6.**Confusion matrix of manual data analysis results with 2% difference threshold rule. The true-positive rate is 100% since the threshold is set for a 100% sensitivity rate. However, due to the high false-positive rate, the overall accuracy is 74.1%, precision is 39.1% and F1 score is 56.3%.

**Figure 7.**Machine learning algorithm result comparison for different sets of features of ABC, DTC, MLPC, and SVC algorithms. The DTC algorithm outperformed all other algorithms with the feature set of F0-F2-F3-F6-F10-F13 by achieving 96.6% overall accuracy.

**Figure 10.**Accuracy and standard deviation results of CNN algorithm with the different time window of the data. The results showed that the highest accuracy with the lowest variation was achieved by using the 0–50 ms portion of the signals.

**Figure 11.**Confusion matrix results for the CNN algorithm with an overall accuracy of 97.20%, specificity of 98.17%, and sensitivity of 96.15%.

**Figure 12.**Performance metric comparison between DTC and CNN to diagnose SARS-CoV-2 with their best performing parameters. While the CNN algorithm outperforms the DTC algorithm in accuracy, precision, sensitivity, specificity, and F1 score.

Samples | Vendor Product | Number of Samples | Label |
---|---|---|---|

SARS-CoV-2 | ATCC VR-1986HK [24] | 400 | Positive |

Blank | NA | 400 | Negative |

SARS-CoV | ZeptoMetrix NATSARS-ST [25] | 400 | Negative |

H-CoV OC43 | ZeptoMetrix 0810024CFHI [26] | 400 | Negative |

MERS-CoV | ZeptoMetrix NATMERS-ST [27] | 400 | Negative |

H1N1 Influenza A | ZeptoMetrix 0810109CFNHI [28] | 400 | Negative |

Sample | Number of Samples | Label |
---|---|---|

SARS-CoV-2 | 800 | Positive |

SARS-CoV | 160 | Negative |

Influenza | 160 | Negative |

H-CoV | 160 | Negative |

MERS-COV | 160 | Negative |

Blank | 160 | Negative |

**Table 3.**List of statistical features extracted from sensor reading that was used to train test machine learning algorithms (ABC, DTC, MLPC, and SVC).

# | Name | Definition | # | Name | Definition |
---|---|---|---|---|---|

F0 | 2% current difference | $Samp\left(1ms\right)-1.02Base\left(1ms\right)$ | F9 | Mean absolute deviation | $\frac{1}{N}{\displaystyle {\displaystyle \sum}_{1}^{N}}\left|x\left(n\right)-\overline{x}\right|$ |

F1 | Maximum value | $\mathrm{max}x\left(n\right)$ | F10 | Median absolute deviation | $\frac{1}{N}{\displaystyle {\displaystyle \sum}_{1}^{N}}\left|x\left(n\right)-{x}_{median}\right|$ |

F2 | Minimum value | $\mathrm{min}x\left(n\right)$ | F11 | Crest Factor | $\frac{max\left(x\left(n\right)\right)}{\sqrt{\frac{1}{N}{{\displaystyle \sum}}_{1}^{N}{\left(x\left(n\right)\right)}^{2}}}$ |

F3 | Mean | $\frac{1}{N}{\displaystyle {\displaystyle \sum}_{1}^{N}}x\left(n\right)$ | F12 | Peak2RMS | $\frac{max\left(\left|x\left(n\right)\right|\right)}{\sqrt{\frac{1}{N}{{\displaystyle \sum}}_{1}^{N}{\left(x\left(n\right)\right)}^{2}}}$ |

F4 | Peak to peak | $F0-F1$ | F13 | Skewness | $\frac{\frac{1}{N}{{\displaystyle \sum}}_{1}^{N}{\left(x\left(n\right)-\overline{x}\right)}^{3}}{\frac{1}{N}{{\displaystyle \sum}}_{1}^{N}{\left(x\left(n\right)-\overline{x}\right)}^{2}}$ |

F5 | Harmonic mean | $\frac{N}{{{\displaystyle \sum}}_{1}^{N}1/x\left(n\right)}$ | F14 | Kurtosis | $\frac{\frac{1}{N}{{\displaystyle \sum}}_{1}^{N}{\left(x\left(n\right)-\overline{x}\right)}^{4}}{{\left(\frac{1}{N}{{\displaystyle \sum}}_{1}^{N}{\left(x\left(n\right)-\overline{x}\right)}^{2}\right)}^{2}}$ |

F6 | Trimmed mean | Mean excluding outliers | F15 | Shape Factor | $\frac{\sqrt{\frac{1}{N}{{\displaystyle \sum}}_{1}^{N}{\left(x\left(n\right)\right)}^{2}}}{\frac{1}{N}{{\displaystyle \sum}}_{1}^{N}\left|x\left(n\right)\right|}$ |

F7 | Variance | $\frac{1}{N}{\displaystyle {\displaystyle \sum}_{1}^{N}}{\left(x\left(n\right)-\overline{x}\right)}^{2}$ | F16 | RMS | $\sqrt{\frac{1}{N}{\displaystyle {\displaystyle \sum}_{1}^{N}}{\left(x\left(n\right)\right)}^{2}}$ |

F8 | Standard deviation | $\sqrt{\frac{1}{N}{\displaystyle {\displaystyle \sum}_{1}^{N}}{\left(x\left(n\right)-\overline{x}\right)}^{2}}$ | Where $x\left(n\right)=Sample\left(n\right)-baseline\left(n\right)$ |

**Table 4.**The list of feature sets that were used in ML algorithms. It was started with all features and the elimination of less informative features was applied.

Eliminated Features | Feature Numbers |
---|---|

None Eliminated | F0 F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13 F14 F15 F16 |

Features with 5 Lowest Scores | F0 F2 F3 F4 F6 F7 F8 F9 F10 F13 F16 |

Features with 10 Lowest Scores | F0 F2 F3 F6 F10 F13 |

Features with 12 Lowest Scores | F0 F3 F6 F13 |

Features with 16 Lowest Scores | F0 |

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**MDPI and ACS Style**

Gecgel, O.; Ramanujam, A.; Botte, G.G.
Selective Electrochemical Detection of SARS-CoV-2 Using Deep Learning. *Viruses* **2022**, *14*, 1930.
https://doi.org/10.3390/v14091930

**AMA Style**

Gecgel O, Ramanujam A, Botte GG.
Selective Electrochemical Detection of SARS-CoV-2 Using Deep Learning. *Viruses*. 2022; 14(9):1930.
https://doi.org/10.3390/v14091930

**Chicago/Turabian Style**

Gecgel, Ozhan, Ashwin Ramanujam, and Gerardine G. Botte.
2022. "Selective Electrochemical Detection of SARS-CoV-2 Using Deep Learning" *Viruses* 14, no. 9: 1930.
https://doi.org/10.3390/v14091930