Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems
Abstract
:1. Introduction
2. Physiology
2.1. Gastric and Pyloroduodenal Region
2.2. Small Intestine
2.3. Ileocecal Region and Colon
3. Effect of Modifiable and Non-Modifiable Factors on Bowel Sounds
3.1. Serum 5-Hydroxytryptamine
3.2. Medications
3.3. Morphine
3.4. Coffee and Soda
3.5. Stress
3.6. Age and Gender
4. Clinical Application of Bowel Sounds
4.1. Intestinal Obstruction
4.2. Acute Appendicitis
4.3. Large Bowel Disorders
4.4. Ascites
4.5. Post-Operative Complications and Critical Care
4.6. Irritable Bowel Syndrome
4.7. Diabetes Mellitus
4.8. Neurodegenerative Disorders
4.9. Neonates
5. Auscultation and Recording Technologies
6. Discussion
Digital Phonoenterography Using Microwave-Based Systems: Future Perspectives
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Year, Author | Study | Technique | Results & Limitations |
---|---|---|---|
1967, Georgoulis [112] | Intestinal sounds classification in post-operative patients. | Capsule microphone → tape recorder → B filter → paper record. | Simple and compound sounds showing interpersonal variation. Requires 48-h long recording. |
1988, Radnitz [113] | Biofeedback with bowel sounds for irritable bowel syndrome patients. | Audio-visual bowel sound recording for training patient’s bowel activity. | Reduced mean daily diarrhea reporting, maintained up to 1 year, affected by stress. |
1998, Hadjileontiadis [114] | Symmetrical alpha-stable distribution for lung sounds and bowel sounds analysis. | 15 to 30 s signal → converter (sampling rate of 2.5 KHz for lung, and 5 KHz for bowel) → WTST-NST and inverse filter. | Contaminated signal- alpha is ~1.5. Denoised signal- alpha decreased significantly. |
1994, Sugrue [115] | Computer aided sound analysis system (C.A.S.A.S) in acute abdomen cases vs. healthy controls. | Microphone → analog to digital converter (ADC) → computerized analysis for bowel sounds features | Increased mean sound length and amplitude, and reduced frequency in cases. Patients need to remain still during recording. |
1999, Hadjileontiadis [61] | Higher order crossings (HOC) in large bowel disorders vs. healthy controls. | Audioscope → WTST-NST filter → Number of axis crossings (equally spaced points of time) counted → HOC pattern plotted. | Post-polypectomy HOC comparable to control, proving efficacy of the procedure. |
2000, Hadjileontiadis [116] | Wavelet based stationary and non-stationary filter (WTST-NST). | Signal divided with wavelet transform (WT) → decomposed into multiple scales with applied power and threshold → filtered with WT coefficient → denoised signal. | Efficiently removed interfering noises and enhanced signal quality. |
2001, Ranta [117] | Bowel sound processing (denoising, segmentation and characterization) based on wavelet-based algorithm (39) | Multiple microphones to localize bowel sounds → wavelet coefficients vector with feature extraction for segmentation | Correct interpretation and decontamination of recorded data needed. |
2003, Hadjileontiadis [118] | Bowel sound enhancement with reduction of background noise. | Kurtosis-based detector → time domain of explosive bowel sounds → separated from background noise. | Reliable detector for extracting bowel sound peaks. |
2003, 2005, Hadjileontiadis [119,120] | To detect explosive lung and bowel sounds in patients with pulmonary and gastrointestinal pathology respectively. | Fractal Dimension (FD) based detector in wavelet transform (WT) domain → detects FD variation and WT coefficients related to lung or bowel sounds. | Low noise susceptibility proved with noise stress test. |
2008, Dimoulas [121] | Autonomous intestinal motility analysis for long-term bowel sound monitoring. | Time-frequency features and wavelet parameters in combination with multi-layer perceptron. | Recognition accuracy of 94.84% and 2.19% error in separating interfering noises |
2008, Hill [76] | Efficacy of a novel device in NICU patients before and after feeding. | Electronic stethoscope → amplifier → acquisition card → computer → picked up hyperactive bowel sound | Significant background noise not accounted for. |
2011, Kim [88] | Modified iterative kurtosis-based detector and estimation algorithms based on regression model of jitter and shimmer. | Piezo-polymer microphone → filtered, digitized, segmented, modified (kurtosis-based algorithm) and characterized (absolute jitter and shimmer method) | Longer colon transit time in delayed bowel motility cases. Small sample size. Lack of technical specifications of the device. |
2011, Kim [89] | Back propagation neural network (BPNN) and Artificial neural network (ANN) | Signal modified (kurtosis-based algorithm) and characterized (absolute jitter and shimmer)→ analyzed using BPNN and ANN model. | Longer colon transit time in delayed gastric emptying and spinal cord injury cases. Short sample size and duration of recordings. |
2011, Tsai [42] | LabVIEW technique for real-time monitoring of bowel sounds. | Electric condenser microphone attached to a stethoscope → data acquisition interface. | Proved the effectiveness of the digital infinite impulse responses (IIR) filter. |
2013, Lin [122] | Higher order statistics based radial basis function network. | A three-layer network with input, hidden and output layers to augment and enhance sound. | Enhancement of bowel sounds during both stationary and non-stationary conditions. |
2013, Sakata [87] | Fasting and post meals bowel sounds in healthy volunteers. | Recording device with sensors and built-in amplifiers → computer with WTST-NST filter | Unsynchronized recording of stethoscope and device with conditions not indicative of normal digestive activities. |
2014, Spiegel [65] | Bowel sounds in patients with post-operative ileus (POI) vs. those tolerating oral feed. | Real time monitoring using a surveillance biosensor. | Intestinal rate of healthy controls → patients tolerating oral feeds → POI. Failed to isolate coordinated bowel activity. |
2015, Mamun [123] | Low power integrated bowel sound measurement system. | Piezoelectric film used as a sensor, amplified, filtered and characterized. | Detected regularly sustained bowel sounds from surrounding noises. |
2015, Longfu [124] | Spectral entropy for bowel sound signal identification. | Dynamic weighing threshold and spectral subtraction for detecting and increasing signal to noise ratio (SNR) | Accurate detection of endpoint of bowel sounds in low SNR condition. |
2014, Sheu [125] | Higher order crossings-based fractal dimension method in noisy conditions | Recorded bowel sounds → analyzed using higher order crossings. | Superior performance to conventional fractal dimension algorithms. |
2015, Yin [126] | Artificial neural network to recognize digestive state. | Extracted bowel sounds → adaptive filtering using 2 reference signals → least mean square algorithm for denoising → threshold detection block | Detected the ongoing digestive state in 3 volunteers. |
2016, Mamun [28] | Ultra-low power real time bowel sound detector to measure meal instances in artificial pancreas device. | Piezoelectric sensor → transduced into voltage signal by front end processor → feature extractor identifies bowel sound segment. | Consumes 53microW power from 1V supply in 0.96 mm2 area. Suitable for portable devices with 85% accuracy and low false positive rates. |
2018, Sato [91] | Non-contact bowel sound analysis after consumption of carbonated water. | Bowel sound segment detection → extraction→ classification → evaluation to detect signal to noise ratio (SNR) | Number of bowel sound segments inversely related to SNR. Accuracy inversely related to post-meals SNR. Small sample size & low sound pressure in stethoscope. |
2018, Liu [127] | Mel Frequency Cepstrum Coefficient Feature (MFCC) and Long Short-Term Memory (LSTM) neural network. | Compressed 1 min voice recording→ screened by two doctors for presence or absence of sound signals → further processing and extraction. | Effective results in same environment; decreased sensitivity with noisy signals. |
2019, Kolle [128] | Filtering of bowel sounds using multivariate empirical mode decomposition. | Model increases the non-linear components of signals and separates them from other signals. | False events identified and filtered out with easy identification of relevant events. Contamination by artefacts. |
2020, Kodani [129] | Long-term bowel sound measurement with elimination of movement-related cloth rubbing noises. | Portable sensor, with the notch, wavelet and low-pass filters → increase focus on bowel sounds and cloth-rubbing noise → separated based on the number of peaks at specific frequency signals. | Effective in differentiating bowel sounds from noise. Difficulty in separating when both overlap. |
2020, Zhao [130] | Long-term bowel sound monitoring with Convolutional Neural Network (CNN). | Wearable bowel sound system used for monitoring and CNNs used for segment recognition. | High sensitivity and moderate accuracy for bowel sound monitoring. Time consuming. Noisy-labels present. |
2020, Zheng [131] | Convolutional Recurrent Neural Network (CRNN) system-based sound detection. | Gastrointestinal sound set with collection instrument, dataset annotation and distribution, to detect bowel sounds, speech, snoring, cough, rub and groan. | Effective in identifying snore and cough. Weak performance due to low frequency of bowel sound. |
2021, Namikawa [132] | Real time bowel sound analysis system for peri-operative monitoring in gastric surgery patients. | Recording equipment and acoustic sensors used to record frequency of bowel sounds. | Frequency of bowel sound was higher in post-gastrectomy cases, with inverse relation to operation time. Small sample size & large-sized equipment. |
2021, Ficek [133] | Hybrid convolutional, recursive neural network for bowel sound analysis. | Intestinal sound contact microphone → analyzed using deep neural network. | Efficiently analyzed bowel sound sequences. Lacks wireless technology. |
2022, Sitaula [84] | Convolutional Neural Network (CNN) to classify neonatal bowel sounds. | Digital stethoscope recording → computer analysis based on CNN system → refined with Laplace hidden semi-Markov model | Classified bowel sounds into peristaltic and non-peristaltic. Imbalanced data without noise cancellation. |
2022, Zhao [22] | Binarized CNN-based BS recognition algorithm with time-domain histogram features for wearable device. | Wearable BS recorder → Gateway via Bluetooth → relayed to cloud servers (wired or wireless) | Algorithm reached 99.92% classification accuracy and very low false alarm rate. Validated by hardware implementation and computation overhead reduction ratio of 58.28 for overall operation. |
2022, Wang [21] | Flexible dual-channel digital auscultation patch with active noise reduction for long-term BS monitoring. | Digital auscultation patch (two channels for BS and one channel for ambient noise) → transmitted via Bluetooth → computer processing with adaptive filtering for active noise reduction, feature extraction and source localization → BS analysis created with intelligent systems. | Flexible, soft, light patch can easily bend to maintain conformal attachment on the abdomen. Wireless wearable device is suitable for long term monitoring. Noise reducing algorithm is useful in noisy clinical environments. |
2022, Kutsumi [111] | Prototype smartphone application to record BS using built-in microphone with automatic analyzation of BS. | BS recorded with built-in microphone of Apple iPhone 7 using the BS recording application. Annotated BS segments were analyzed using CNN and LSTM models. | The CNN model was superior and recognized BS with moderate accuracy (88.9%) with data recorded from a smartphone. |
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Redij, R.; Kaur, A.; Muddaloor, P.; Sethi, A.K.; Aedma, K.; Rajagopal, A.; Gopalakrishnan, K.; Yadav, A.; Damani, D.N.; Chedid, V.G.; et al. Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems. Sensors 2023, 23, 2302. https://doi.org/10.3390/s23042302
Redij R, Kaur A, Muddaloor P, Sethi AK, Aedma K, Rajagopal A, Gopalakrishnan K, Yadav A, Damani DN, Chedid VG, et al. Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems. Sensors. 2023; 23(4):2302. https://doi.org/10.3390/s23042302
Chicago/Turabian StyleRedij, Renisha, Avneet Kaur, Pratyusha Muddaloor, Arshia K. Sethi, Keirthana Aedma, Anjali Rajagopal, Keerthy Gopalakrishnan, Ashima Yadav, Devanshi N. Damani, Victor G. Chedid, and et al. 2023. "Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems" Sensors 23, no. 4: 2302. https://doi.org/10.3390/s23042302
APA StyleRedij, R., Kaur, A., Muddaloor, P., Sethi, A. K., Aedma, K., Rajagopal, A., Gopalakrishnan, K., Yadav, A., Damani, D. N., Chedid, V. G., Wang, X. J., Aakre, C. A., Ryu, A. J., & Arunachalam, S. P. (2023). Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems. Sensors, 23(4), 2302. https://doi.org/10.3390/s23042302