# RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices

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## Abstract

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## 1. Introduction

## 2. System Design

#### 2.1. Transmitter

#### 2.2. Wireless Channel

#### 2.3. Receiver

## 3. Methodology

- Breathing data collection;
- Breathing data extraction;
- Breathing data processing;
- Breathing pattern classification.

#### 3.1. Breathing Data Collection

#### 3.2. Breathing Data Extraction

#### 3.3. Breathing Data Processing

#### 3.3.1. Subcarrier Selection

#### 3.3.2. Removing Outliers

#### 3.3.3. Smoothening Data

#### 3.3.4. Feature Extraction

#### 3.4. Classification

## 4. Results and Discussions

#### 4.1. Breathing Pattern Detection

- Eupnea is breathing with a normal pattern and rate. Eupnea is usually 12–20 breaths per minute for adults. For this breathing pattern, the subject was requested to breathe normally at a normal rate. From Figure 5a, it is seen that there were 10 breaths per 30 s, which agrees with the breathing patterns shown in Figure 1a.

#### 4.2. Abnormal Breathing Patterns Classification

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 4.**Breathing data processing: (

**a**) data after subcarrier selection; (

**b**) data after outlier removal; (

**c**) data after smoothing.

**Figure 5.**Detection of different abnormal breathing patterns: (

**a**) eupnea; (

**b**) bradypnea; (

**c**)tachypnea; (

**d**) biot; (

**e**) sighing; (

**f**) kussmaul.

Sr. # | Breathing Pattern Type | Description | Causes |
---|---|---|---|

1. | Eupnea | Normal breathing pattern and rate | Balanced diet and healthy life |

2. | Bradypnea | Slow and shallow breathe | Sleep drugs, metabolic disorder, head injury, stroke |

3. | Tachypnea | Fast and shallow breathe | Fever, anxiety, exercise, shock |

4. | Biot | Deep breathe with gradual periods of no breaths | Spinal meningitis, head injury |

5. | Sighing | Breathing punctuated by frequent deep breathes | Anxiety, dyspnea, and dizziness |

6. | Kussmaul | Fast and deep breaths | Renal failure, metabolic acidosis, diabetic ketoacidosis |

Sr. No. | Subject | Age (Years) | Height (cm) | Weight (Kg) | Body Structure |
---|---|---|---|---|---|

1 | Male | 28 | 179 | 65 | Ectomorph |

2 | Male | 31 | 176 | 52 | Endomorph |

3 | Male | 26 | 173 | 76 | Endomorph |

4 | Male | 31 | 177 | 52 | Ectomorph |

5 | Male | 31 | 174 | 65 | Endomorph |

Sr. # | Statistical Features | Description | Expression |
---|---|---|---|

1 | Minimum | Minimum value of data | ${Y}_{min}=min\left({y}_{i}\right)$ |

2 | Maximum | Maximum value of data | ${Y}_{max}=max\left({y}_{i}\right)$ |

3 | Mean | Mean of data | ${Y}_{m}=\frac{1}{N}{\displaystyle {\displaystyle \sum}_{i=1}^{N}}{y}_{i}$ |

4 | Variance | degree of data spread | ${Y}_{SD}={\displaystyle {\displaystyle \sum}_{i=1}^{n}}{({y}_{i}-{Y}_{m})}^{2}$ |

5 | Standard deviation | Square root of variance | ${Y}_{v}=\sqrt[2]{\frac{1}{N-1}{\displaystyle {\displaystyle \sum}_{i=1}^{N}}{\left({y}_{i}-{Y}_{m}\right)}^{2}}$ |

6 | RMS | Root mean square data | ${Y}_{RMS}=\sqrt[2]{\frac{1}{N}{\displaystyle {\displaystyle \sum}_{i=1}^{N}}{y}_{i}{}^{2}}$ |

7 | Peak-to-peak value | Data fluctuations about the mean | ${Y}_{p-p}={Y}_{max}-{Y}_{min}\left(i=1,2,\dots ,N\right)$ |

8 | Kurtosis | Measure of tailedness in data | ${Y}_{K}=\frac{\frac{1}{K}{{\displaystyle \sum}}_{i=1}^{K}{\left(\left|\begin{array}{c}{y}_{i}\end{array}\right|-{Y}_{m}\right)}^{4}}{{Y}_{RMS}{}^{4}}$ |

9 | Skewness | Measure of symmetry in data | ${Y}_{S}=\frac{\frac{1}{N}{{\displaystyle \sum}}_{i=1}^{N}{\left(\left|\begin{array}{c}{y}_{i}\end{array}\right|-{Y}_{m}\right)}^{3}}{{Y}_{RMS}{}^{3}}$ |

10 | Peak factor | Ratio of maximum data value to RMS | ${Y}_{P}=\frac{max\left({y}_{i}\right)}{{Y}_{RMS}}\left(i=1,2,\dots ,N\right)$ |

11 | Interquartile range | Mid-spread of data | ${Y}_{IQ}={Q}_{3}-{Q}_{1}$ |

12 | Waveform factor | Ratio of the RMS value to the mean value | ${Y}_{W}=\frac{{Y}_{RMS}}{{Y}_{M}}$ |

13 | FFT | Frequency information about data | ${Y}_{FFT}={\displaystyle {\displaystyle \sum}_{n=-N}^{N}}y\left(n\right){e}^{-j\frac{2\pi}{N}nk}$ |

14 | Frequency Min | Minimum Frequency component | ${Y}_{fmin}=Min\left({Y}_{FFT}\right)$ |

15 | Frequency Max | Maximum Frequency component | ${Y}_{fmax}=Max\left({Y}_{FFT}\right)$ |

16 | Spectral Probability | Probability distribution of spectrum | ${Y}_{SP}=\frac{FFT{\left(d\right)}^{2}}{{{\displaystyle \sum}}_{i=-N}^{N}FFT{\left(i\right)}^{2}}$ |

17 | Signal Energy | Measure of energy component | ${Y}_{SE}={\displaystyle {\displaystyle \sum}_{n=-N}^{N}}{\left|p\left(d\right)\right|}^{2}$ |

18 | Spectrum Entropy | Measure of data irregularity | ${Y}_{H}={\displaystyle {\displaystyle \sum}_{i=-N}^{N}}p\left(d\right)\mathrm{ln}\left(p\left(d\right)\right)$ |

Algorithms | Actual/Predicted | Eupnea | Bradypnea | Tachypnea | Biot | Sighing | Kussmaul |
---|---|---|---|---|---|---|---|

Complex Tree | Eupnea | 3608 | 33 | 0 | 9 | 0 | 0 |

Bradypnea | 40 | 3604 | 0 | 6 | 0 | 0 | |

Tachypnea | 0 | 0 | 3632 | 0 | 0 | 18 | |

Biot | 9 | 3 | 0 | 3638 | 0 | 0 | |

Sighing | 0 | 0 | 0 | 0 | 3649 | 1 | |

Kussmaul | 0 | 0 | 4 | 0 | 1 | 3645 | |

Ensemble Subspace KNN | Eupnea | 3509 | 18 | 59 | 3 | 0 | 1 |

Bradypnea | 29 | 3594 | 23 | 0 | 0 | 4 | |

Tachypnea | 99 | 37 | 3505 | 9 | 0 | 0 | |

Biot | 1 | 0 | 1 | 3638 | 0 | 4 | |

Sighing | 0 | 0 | 0 | 0 | 3650 | 0 | |

Kussmaul | 2 | 5 | 1 | 1 | 0 | 3641 | |

Quadratic SVM | Eupnea | 3403 | 185 | 0 | 61 | 1 | |

Bradypnea | 160 | 3485 | 0 | 5 | 0 | 0 | |

Tachypnea | 18 | 6 | 3589 | 18 | 0 | 19 | |

Biot | 117 | 2 | 0 | 3531 | 0 | 0 | |

Sighing | 0 | 0 | 0 | 0 | 3650 | 0 | |

Kussmaul | 0 | 2 | 0 | 4 | 2 | 3642 | |

Coarse KNN | Eupnea | 3278 | 137 | 109 | 101 | 0 | 25 |

Bradypnea | 85 | 3436 | 104 | 2 | 0 | 23 | |

Tachypnea | 195 | 134 | 3197 | 49 | 0 | 75 | |

Biot | 46 | 1 | 40 | 3523 | 10 | 30 | |

Sighing | 0 | 0 | 0 | 0 | 3649 | 1 | |

Kussmaul | 14 | 51 | 0 | 21 | 20 | 3544 |

Algorithms | Accuracy (%) | Prediction Speed (obs/s) | Training Time (s) |
---|---|---|---|

Complex Tree | 99.4 | ~330,000 | 7.58 |

Ensemble Subspace KNN | 98.6 | ~12,000 | 239.40 |

Quadratic SVM | 97.3 | ~21,000 | 193.92 |

Coarse KNN | 94.2 | ~2400 | 182.18 |

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## Share and Cite

**MDPI and ACS Style**

Rehman, M.; Shah, R.A.; Khan, M.B.; AbuAli, N.A.; Shah, S.A.; Yang, X.; Alomainy, A.; Imran, M.A.; Abbasi, Q.H.
RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices. *Sensors* **2021**, *21*, 3855.
https://doi.org/10.3390/s21113855

**AMA Style**

Rehman M, Shah RA, Khan MB, AbuAli NA, Shah SA, Yang X, Alomainy A, Imran MA, Abbasi QH.
RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices. *Sensors*. 2021; 21(11):3855.
https://doi.org/10.3390/s21113855

**Chicago/Turabian Style**

Rehman, Mubashir, Raza Ali Shah, Muhammad Bilal Khan, Najah Abed AbuAli, Syed Aziz Shah, Xiaodong Yang, Akram Alomainy, Muhmmad Ali Imran, and Qammer H. Abbasi.
2021. "RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices" *Sensors* 21, no. 11: 3855.
https://doi.org/10.3390/s21113855