Editorial Feature

Wearable E-Nose Sensor Detects Liver Dysfunction

Metabolic disorders are often associated with typical odors that can be identified in exhaled breath, sweat, or other human excrements. Ammonia odor, which is associated with renal diseases, and acetone odor, which is associated with diabetes, are two examples.

Wearable E-Nose Sensor Detects Liver Dysfunction

Image Credit: mi_viri/Shutterstock.com

Advancements in materials, sensors, electronics, and signal processing technologies have led to an exponential increase in the implementation and advancement of e-noses in recent decades. In many areas of science and industry, e-noses are used to analyze, identify, distinguish, categorize, and evaluate gas components or odors, among other things, and are of interest for a variety of applications.

For instance, in the food and beverage industry, e-noses are used to track production and define the quality of the finished product, while in pharmaceutical science, e-noses are used for formulation development and quality confirmation, as well as for air quality monitoring. Furthermore, e-noses are used in agriculture, water management, medicine, security systems, and a variety of other fields.

As a result, the focus of research published in the MDPI journal Biosensors was to see if liver dysfunction is generally recognized and if the level of abnormality can be classified using a wearable semiconducting MOx gas sensor-based e-nose in the context of experimental research.

Methodology

A system called “LiverTracer” was created as part of this research. It is based on an e-nose system that identifies and categorizes changes in VOCs from exhaled breath caused by liver issues and problems. As shown in Figure 1, this system consists of a measuring head that houses the sensor array and acts as a foundation for measurement control and data analysis.

Setup of the electronic nose system “LiverTracer.”

Figure 1. Setup of the electronic nose system “LiverTracer.” Image Credit: Voss, et al. 2022

The basic unit was a “SPIROSTIK COMPLETE” spirometer. It includes a computer running Windows 10. This device was altered to meet the needs of the researcher.

On the software side, sensor management, data management, operator assistance (semi-automatic patient measurement), and data analysis were developed and integrated. Whereas, on the hardware side, the pump system for rinsing and readjusting the measuring head was developed and integrated. Figure 2 depicts the measurement regime’s basic principles.

Flow chart of the LiverTracer measurement regime.

Figure 2. Flow chart of the LiverTracer measurement regime. Image Credit: Voss, et al. 2022

MATLAB R2019a was used to analyze the data. Outliers, technical issues, artifacts, and measurement errors were identified in the nine raw resistance waveforms. There were no measurements that had to be discarded. The relevant 30-second segments were derived from the measurement for the analysis of the corresponding breathing air segments, as shown in Figure 3b, and are denoted by vertical dashed lines.

This was done automatically based on the temperature measurement protocol, which clearly shows where the breath measurement begins and ends in Figure 3a, “bc1” and “bc2.”

(a) Schematic representation of the measurement protocol based on a predefined temperature control of the sensor heater. It contains time-variable cyclic thermal cleaning cycles “tc,” burn-off cleaning phases “bf” (rectangle functions), subsequent flushing phases “fp” (horizontal lines), one ambient air measurement cycle “ac,” and two breathing gas measurement cycles “bc1” and “bc2.” The arrows mark the exhalation cycles (patient breathing: PB); (b) example of a recording of 9 sensor layer resistance curves. Vertical dashed lines mark the two breathing gas measurements.​​​​​​​

Figure 3. (a) Schematic representation of the measurement protocol based on a predefined temperature control of the sensor heater. It contains time-variable cyclic thermal cleaning cycles “tc,” burn-off cleaning phases “bf” (rectangle functions), subsequent flushing phases “fp” (horizontal lines), one ambient air measurement cycle “ac,” and two breathing gas measurement cycles “bc1” and “bc2.” The arrows mark the exhalation cycles (patient breathing: PB); (b) example of a recording of 9 sensor layer resistance curves. Vertical dashed lines mark the two breathing gas measurements. Image Credit: Voss, et al. 2022

The data analysis is on the basis of the extraction of 10 features (in the time and nonlinear dynamics domains) from the resistance time series of the derived breathing gas measurement loops for every sensor layer. Figure 4 depicts the characteristics computed in this domain.

Time domain features extracted from the resistance curve of an exhalation cycle.

Figure 4. Time domain features extracted from the resistance curve of an exhalation cycle. Image Credit: Voss, et al. 2022

Between October 2019 and March 2020, 30 people were enrolled in the study, with 10 being healthy controls, 10 having compensated cirrhosis, and 10 having decompensated cirrhosis. The observations revealed that vital parameters at inclusion did not vary between these groups, as shown in Table 1.

Table 1. Patient data (values in parentheses represent the respective minimum and maximum values or describe percentages). Source: Voss, et al. 2022

  Control (n = 10) Compensated
Cirrhosis (n = 10)
Decompensated
Cirrhosis (n = 10)
p-Value
Sex (f/m) 5/5 3/7 2/8 0.500
Age (years) 58 (51; 65) 57 (52; 64) 62 (56; 67) 0.543
Bodyweight (kg) 81 (68; 96) 94 (79; 101) 80 (68; 97) 0.136
Height (cm) 175 (167; 178) 176 (167; 178) 176 (169; 181) 0.712
Smoker (n,%) 1 (10%) 4 (40%) 3 (30%) 0.450
Vital signs
RR systolic (mmHg)
135 (118; 161) 126 (107; 155) 122 (103; 136) 0.266
RR diastolic (mmHg) 81 (76; 104) 76 (61; 92) 72 (63; 79) 0.146
Heart rate (pbm) 78 (67; 102) 85 (71; 88) 92 (81; 104) 0.212
Temperature (°C) 36.8 (3.4; 37.0) 36.6 (36.1; 37.0) 36.7 (36.4; 37.1) 0.523
Etiology of cirrhosis (n,%)
Ethanol
N/A 6 (60%) 8 (80%) 0.628
Other N/A 4 (40%) 2 (20%)  
Co-medication (n,%)
Lactulose
1 (10%) 3 (30%) 8 (80%) 0.009
Proton pump inhibitors 5 (50%) 7 (70%) 9 (90%) 0.262
B-Blocker 5 (50%) 4 (40%) 5 (50%) 0.897
Antibiotics 1 (10%) 3 (30%) 7 (70%) 0.016
- Rifaximin 0 1 (10%) 6 (60%)  
- other 1(10%) 2 (20%) 1 (10%)  

 

f—females; m—males; n—number of patients; p—significance.

Results and Discussion

Tables 2 and 3 show the classification results of the LiverTracer e-nose. The detachment of the patient groups from the controls, as shown in Table 2, was 100% successful for each case. Between the patient groups, a 95% correct classification rate was achieved, with 90% of the DECOMP group and 100% of the COMP group correctly classified.

Table 3 displays the descriptive statistics of sensor characteristics that were instantly chosen by the discriminant analysis to achieve the best detachment outcomes.

Table 2. Percentage classification rate of e-nose features. The optimal parameter set (consisting of either double or triple sets) is shown for each group comparison. Source: Voss, et al. 2022

Group Features SENS SPEC ACC AUC
CON—COMP RS11_s_slope_maxmin (Ohm/s)
RS32_area3sec_9 (Ohm·s)
RS32_p00
1.00 1.00 1.00 1.00
CON—DECOMP RS31_slope_startmax (Ohm/s)
RS32_s_slope_startmax_pos (s)
RS33_p00
1.00 1.00 1.00 1.00
COMP—DECOMP RS32_Renyi4_entropy (bit)
RS33_area2 (Ohm·s)
0.90 1.00 0.95 0.97

 

CON—control group; COMP—patients with compensated cirrhosis; DECOMP—patients with decompensated cirrhosis; RSxy—R denotes resistance measurement values of sensor layer y of sensor Sx (e.g., RS12 describes the resistance readings of sensor layer 2 of sensor S1); SENS— sensitivity; SPEC—specificity; ACC—Accuracy; AUC—area under the receiver operator characteristic curve.

Table 3. Classification results of features automatically selected by discriminant analysis (mv—mean value, sd—standard deviation). Source: Voss, et al. 2022

Group Test Features p CON
mv ± sd
COMP
mv ± sd
DECO
MP mv ± sd
CON vs. RS11_s_slope_maxmin (Ohm/s) 0.046 -86,258 ± 5225 -81,023 ± 5676  
COMP RS32_area3sec_9 (Ohm·s) 0.038 1,807,616 ± 207,540 2,071,884 ± 309,151  
  RS32_p00 0.017 0.336 ± 0.050 0.276 ± 0.045  
CON vs. RS31_slope_startmax (Ohm/s) 0.029 8901 ± 3207   6956 ± 1845
DECOMP RS32_s_slope_startmax_pos (s) 0.019 6.250 ± 1.161   6.900 ± 0.211
  RS33_p00 0.041 0.369 ± 0.045   0.319 ± 0.056
COMP vs. RS32_Renyi4_entropy (bit) 0.028   1.843 ± 0.386 2.179 ± 0.185
DECOMP RS33_area2 (Ohm·s) 0.131   48,252 ± 23,296 34,507 ± 14,547

 

CON—control group; COMP—patients with compensated cirrhosis; DECOMP—patients with decompensated cirrhosis; RSxy—R denotes the resistance measurement values of sensor layer y of sensor Sx (e.g., RS12 describes the resistance readings of sensor layer 2 of sensor S1); p—significance value; mv ± sd—mean value ± standard deviation.

Researchers included four clinical parameters for categorization in Table 4, which are based on the Child-Pugh score and portray multiple elements of liver disease, such as two laboratory values and two clinical aspects.

Table 4. Classification rate (in %) of the clinical parameters that achieved an overall accuracy for discriminating the groups greater than 50%. Source: Voss, et al. 2022

  Categorized Bilirubin Categorized INR Ascites Hepatic Encephalopathy
CON 100 86 100 100
COMP 10 40 70 100
DECOMP 90 60 50 50
ACC 63 59 73 83

 

CON—control group; COMP—patients with compensated cirrhosis; DECOMP—patients with decompensated cirrhosis; INR—international normalized ratio of blood clotting test; ACC—Accuracy.

This exploratory pilot study collected and analyzed distinctive VOC fingerprints in patients’ breath and delivers strong indications that VOC analysis using MOx sensors is a significant diagnostic technique for finding various degrees of liver failure.

The findings from the current study are very promising, indicating that the MOx multisensory signals are primarily useful in the analysis of breath changes and, thus, for the recognition of liver dysfunctions. MOx semiconductor sensors are the most common sensors used in e-noses for diagnostic purposes.

They have good sensitivity, are long-lasting, and, perhaps most notably, are reasonably priced. When considering major commercial deployment, price is an important consideration, particularly in developing countries. Furthermore, since they can operate in a broad range of relative humidity, they are ideal for outdoor usage.

Conclusion

The multisensory analyses conducted in this study using a wearable MOx sensor array demonstrated high detachment accuracies ranging from 95% to 100% between the studied groups.

It was not only useful to distinguish liver dysfunctions of varying severity from controls at 100%, but also to differentiate between the severities of liver dysfunction at 95% with 100% accurate identification of all COMP cirrhosis and 90% correct identification of all DECOMP cirrhosis.

When merged with nonlinear sensor signal processing, the wearable e-nose system for detecting disease—in this case, liver dysfunction—offers numerous advantages over traditional laboratory testing and the use of other sensor systems.

Journal Reference:

Voss, A., Schroeder, R., Schulz, S., Haueisen, J., Vogler, S., Horn, P., Stallmach, A., Reuken, P. (2022) Detection of Liver Dysfunction Using a Wearable Electronic Nose System Based on Semiconductor Metal Oxide Sensors. Biosensors, 12(2), p. 70. Available Online: https://www.mdpi.com/2079-6374/12/2/70.

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Megan Craig

Written by

Megan Craig

Megan graduated from The University of Manchester with a B.Sc. in Genetics, and decided to pursue an M.Sc. in Science and Health Communication due to her passion for combining science with content creation. As part of her studies, Megan partnered with Jodrell Bank Discovery Centre as a Digital Marketing Assistant, producing content and updating sections of their website. In her spare time, she loves to travel, exploring each location's culture and history - including the local cuisine. Her other interests include embroidery, reading fiction, and practicing her Japanese language skills.

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