Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review
Abstract
:1. Introduction
2. Common Deep Learning Architectures in Biosensing
2.1. Convolutional Neural Networks (CNNs)
2.2. Long Short-Term Memory Networks (LSTMs)
2.3. Autoencoders
2.4. Transformers
2.5. Model Selection Considerations
2.6. Commercial Use of Deep Learning Models for Biosensing
3. Sensor Modalities and Corresponding Health Applications
3.1. Deep Learning with EEG
3.2. Deep Learning with EDA
3.3. Deep Learning with ECG
3.4. Consideration for Selecting Biosensors for a Digital Health Application
4. Digital Health Applications using Biosensors
4.1. Remote Patient Monitoring
4.2. Digital Diagnostics
4.3. Adaptive Digital Interventions
4.4. Considerations for Integrating Wearable Sensors in Digital Health Systems
5. Challenges and Opportunities
5.1. Challenges
5.2. Opportunities
6. Conclusions
Funding
Acknowledgments
Conflicts of Interest
References
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Neural Networks | Application | Ref. No. | Dataset Sample Size | Performance |
---|---|---|---|---|
CNN | motor imagery classification | [23] | 9 [24] | accuracy: |
seizure classification | [25] | 352 [26] | accuracy: | |
Alzheimer’s and mild cognitive impairment classification | [27] | 90 | accuracy: | |
mental state classification | [28] | 12/15 [29] | accuracy from [28]: and accuracy from [29]: | |
mild cognitive impairment classification | [30] | 36 | accuracy, sensitivity, specificity, and AUC all above | |
Alzheimer’s classification | [31] | 48 | accuracy: and F1-score: | |
depression classification | [32] | 92 | accuracy: and F1-score: | |
sleep stage classification | [33] | 100 [34] | accuracy: | |
quiet neonatal sleep classification | [35] | 19 [34] | accuracy: | |
error-related potential (ErrP) classification | [36] | 6 | accuracy: 86.46% | |
sleep spindle classification | [37] | 141 | sensitivity: 91.9% to 96.5% and specificity: 95.3% to 96.7% | |
sleep apnea classification | [38] | 2650 [39] | accuracy: 69.9% | |
Parkinson’s classification | [40] | 15 [41] | AUC: 0.99 | |
LSTM | eye-blink and muscular artifact classification | [42] | 40 [43] | accuracy: |
sleep stage classification | [44] | 40 [45] | PCC for 4-class classification: and 2-class classification: | |
emotion quantification from facial expressions | [46] | 28 [47] | RMSE: | |
neurodegenerative disease classification | [48] | 68 | accuracy: | |
ischemic stroke subtype classification | [49] | 2310 | AUC: | |
early mild cognitive impairment classification | [50] | 27 [51] | accuracy: , sensitivity: , and specificity: | |
epileptic seizure detection | [52] | 10 | accuracy for three-class and four-class: 95%, accuracy for five-class: 93.5% | |
mental task classification | [53] | 32 | F1-score: 85% | |
driver fatigue detection | [54] | 23 | accuracy: 87.3% | |
focal and feneralized epilepsy detection | [55] | 50 | accuracy: 96.1% | |
cyclic alternating pattern (CAP) classification | [56] | 16 | ROC: 0.82, accuracy: ranging from 77% to 79% | |
stress classification | [57] | 40 | accuracy: 93.17% | |
Autoencoder | epileptic seizure detection | [58] | 5 [26] | 2-Class classification accuracy: and multi-class classification accuracy: |
motor imagery classification | [59] | 9 [60]/14 [61] | accuracy from first dataset: and accuracy from second dataset: | |
epileptic seizure detection | [62] | 5 [63]/21 [64] | accuracy from [63]: and accuracy from [64]: | |
Alzheimer’s classification | [27] | 90 | accuracy: | |
sleep apnea classification | [65] | 994 [66] | accuracy: | |
epilepsy classification | [67] | 23 | accuracy: | |
Transformer | Alzheimer’s classification | [68] | 88 [69] | accuracy: |
seizure detection | [70] | 22 [71] | accuracy: and F1-score: | |
steady-state visual evoked potential (SSVEP) classification | [72] | 10 [73]/35 [74] | accuracy from [73]: and accuracy from [74]: | |
epilepsy detection | [75] | 121 [76] | accuracy: | |
sleep stage classification | [77] | 21 [78] | accuracy: and F1-score: | |
seizure detection | [79] | 23 | accuracy: 96.15% |
Neural Networks | Application | Ref. No. | Dataset Sample Size | Performance |
---|---|---|---|---|
CNN | emotion classification | [82] | 32 [83] | F1-score for valence and arousal: % and %, respectively |
acute pain classification | [84] | 38 [85] | accuracy: % | |
stress detection | [86] | 15 [87] | RMSE: less than | |
stress detection | [88] | 15 [89] | accuracy: % and F1-Score: % | |
heat-induced pain classification | [90] | 10 | accuracy: | |
LSTM | acute pain classification | [84] | 38 [85] | accuracy: % and F1-score: % |
cybersickness classification | [91] | 9 [92] | accuracy: % | |
pain intensity classification | [93] | 29 [94] | F1-score: 81% and AUC: 0.93 | |
skin hydration level classification | [95] | 16 [96] | accuracy: 97.83% | |
Autoencoder | stress detection | [97] | 58 [98]/ | |
62 [99]/ | accuracies for [98,99,100,101]: %, %, 88% and %, respectively; | |||
22 [100]/ | F1-scores for [98,99,100,101]: , , and , respectively | |||
48 [101] | ||||
epileptic seizure detection | [102] | 166 | sensitivity: 83.9% and false positive rate: 35.3% | |
Transformer | stress detection | [103] | 14 [103] | accuracy for 2-class task: %, 3-class task: %, and 4-class task: % |
Neural Networks | Application | Ref. No. | Dataset Sample Size | Performance |
---|---|---|---|---|
CNN | arrhythmia detection | [108] | 47 [45,109] | 1D CNN accuracy: and 2D CNN accuracy: |
multi-class arrhythmia detection | [110] | 6877 [110] | F1-score: | |
myocardial infarction detection | [111] | 290 [45,109] | accuracy: | |
myocardial infarction detection | [112] | 11 [45,109] | accuracy with noise: % and without noise: % | |
atrial fibrillation detection | [113] | 89 [114] | specificity: and sensitivity: | |
heart abnormality classification | [115] | 480 | accuracy: | |
short-term atrial fibrillation detection | [116] | 25 [117] | F1-score: | |
automatic arousal detection | [118] | 6600 [119] | AUC: | |
acute coronary syndrome-related disease classification | [120] | - | accuracy: | |
sleep classification | [121] | 136 | accuracy: 86.3% | |
ADHD and CD classification | [122] | 123 | accuracy: 96.04% | |
stress detection | [123] | - | accuracy: 88.4% | |
shockable arrhythmia classification | [124] | 18 | AUC: 0.995 | |
driver arrhythmia classification | [125] | - | accuracy: 88.99% | |
cardiovascular disease classification | [126] | 10,646 | accuracy: 95.08% | |
arrhythmia detection | [127] | 47 [117]/290 [128] | accuracy for [117]: 98.66% and accuracy for [128]: 95.79% | |
inter-patient ECG classification and arrhythmia detection | [129] | 47 [117] | accuracy: 98.18% | |
cardiac rhythm classification | [130] | 1928 [131] | F1-score: 89% | |
sleep apnea detection | [132] | 70 [133] | per-recording accuracy: 100% and per-minute accuracy: 85.8% | |
LSTM | arrhythmia classification | [134] | 47 [45,109] | accuracy: |
ECG signal classification | [135] | 47 [45,109] | accuracy: % | |
heart failure classification | [136] | 40,000 [137] | accuracy: 99.09% | |
real-time anomaly detection and classification of 1D ECG signals | [138] | 162 | accuracy: 100% | |
premature ventricular contraction | [139] | 47 [109] | accuracy: 98.5% | |
atrial fibrillation detection | [140] | 47 [109] | accuracy: 93.05% | |
Autoencoder | cardiac arrhythmia classification | [141] | 47 [45,109] | accuracy for VEB: % and accuracy for SVEB: % |
QRS detection | [142] | 47 [45,109] | accuracy: % | |
detection and localization of myocardial infarction | [143] | 52 [45]/148 [144] | MI detection accuracy: % and MI localization accuracy: >99% | |
ECG beat classification | [145] | 47 [45,109] | OAA-MLP accuracy: % and OAO-MLP accuracy: % | |
anomaly detection | [146] | 47 [45,109] | F1-score: 93% | |
ECG heartbeat classification | [147] | 47 [148] | accuracy: | |
atrial fibrillation classification | [149] | 25 [45] | accuracy: | |
beat-by-beat atrial fibrillation detection | [150] | 12,186 [151]/25 [109] | F1-score for [151]: 88% and F1-score for [109]: 87% and | |
heart abnormalities detection | [152] | 105 [153] | accuracy: 98.59% | |
Transformer | arrhythmia classification | [154] | 6877 [155] | F1-Score: |
ECG heartbeat classification | [156] | 10 [157] | accuracy: % | |
arrhythmia detection | [158] | 47/25 [45,109] | 4-categories accuracy: %, 8-categories accuracy: %, and binary classification accuracy: % | |
heartbeat arrhythmia classification | [159] | 337 | accuracy: | |
ECG classification | [160] | 110 [155] | accuracy: and F1-score: | |
stress detection | [161] | 15 [87] | F1-score: | |
classification of tetanus severity | [162] | 110 [163] | F1-score: 88% | |
inter-patient congestive heart failure detection | [164] | 18 [45]/15 [165] | accuracy: 98.88% |
Reference No. | Application |
---|---|
[166] | continuous remote patient monitoring in heart failure management |
[167] | remote patient monitoring optimization in IoMT networks |
[168] | energy-efficient patient monitoring in IoHT networks |
[169] | ambient intelligent system for psychiatric emergencies |
[170] | stroke volume monitoring in congenital heart disease via wearable technology |
[172] | COVID-19 decompensation detection via wearable biosensors |
[173] | evaluating remote patient monitoring and education technology for COVID-19 symptoms |
[174] | IoT-Aware smart hospital system for patient and asset monitoring |
[175] | remote human vital signs monitoring with a 77 GHz FMCW radar |
Reference No. | Application |
---|---|
[179] | early detection of acute myocardial infarction |
[178] | real-time early detection of cadaverine for periodontal disease diagnostics and personalized treatment plans |
[176] | simultaneous detection of protein biomarkers in urine in point-of-care settings |
[177] | heart failure diagnosis with electrochemical sensor from biomarkers in saliva |
[180] | detection of BNP biomarkers in serum |
[181] | alternative to PCR tests to detect coronaviruses, including MERS-CoV and SARS-CoV-2 |
[182] | detection of SARS-CoV-2 and influenza using liquid-gated graphene field-effect transistors |
[183] | public health surveillance through pathogen detection in water |
[184] | detection of serum amyloid A (SAA) and C-reactive protein (CRP) biomarkers |
Ref. No. | Clinical Endpoint | Application Effectiveness | Summary |
---|---|---|---|
[189] | Moderate to Vigorous PA (MVPA) | 4.3 min/week increase vs. controls | The intervention using a wearable device (Fitbit One) and SMS prompts showed a short-term increase in physical activity among overweight and obese adults, but the effect was not sustained beyond the first week of the 6-week study period. |
[190] | Atrial Fibrillation Detection Rate | 9.4% enrollment increase with optimized campaign vs. baseline | The mSToPS trial focused on screening for undiagnosed atrial fibrillation (AF) using a wearable ECG sensor patch, targeting individuals at increased risk. The study emphasized the importance of early detection of AF, a significant contributor to stroke and mortality, to potentially initiate preventative treatment and reduce health risks. |
[191] | Heart Rate (Awake/Asleep) | Detected clenbuterol effect from Day 3 (Awake, +8.79 bpm, ) and Night 1 (Asleep, +3.79 bpm, ) | This study successfully demonstrated the potential of using smartwatch-based heart rate monitoring to detect clenbuterol-induced changes in heart rate during clinical trials, proving particularly effective and sensitive while participants were asleep. |
[192] | Global Perceived Effect (GPE) | Higher in VRRS () vs. Control (); p < | The study demonstrated that early virtual-reality-based home rehabilitation (VRRS) after Total Hip Arthroplasty was as effective as traditional rehabilitation in improving functional outcomes, with participants in the VRRS group expressing higher satisfaction with their rehabilitation program. This indicates that VRRS can be a viable and patient-preferred alternative to conventional methods, warranting further exploration. |
[193] | Influenza Prediction Accuracy | 81% accuracy 2 days before major symptoms | The study demonstrated employing wearable technology for continuous monitoring of physiological parameters for early flu detection and surveillance, offering insights into the natural progression of the disease and facilitating timely healthcare interventions during outbreaks. |
[194] | System Compliance | Median 11.57 days of use out of 14-day period (87% completion rate) | This study validated the mHealth system’s ability to passively and unobtrusively monitor and evaluate Parkinson’s disease symptoms, including an evaluation algorithm, indicating its potential to enhance disease management and patient care in real-life settings. Future research is needed to confirm these benefits and to further explore the system’s impact on disease management. |
[195] | Medication Adherence | 30% increase in confirmed daily doses with WOT (93% with WOT vs. 63% with DOT) | The study demonstrated that Wirelessly Observed Therapy (WOT), a digital patient self-management system involving an edible ingestion sensor, wearable patch, and mobile device, accurately detected medication ingestions and confirmed daily adherence to tuberculosis (TB) treatment more effectively than Directly Observed Therapy (DOT). |
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Islam, T.; Washington, P. Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review. Biosensors 2024, 14, 183. https://doi.org/10.3390/bios14040183
Islam T, Washington P. Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review. Biosensors. 2024; 14(4):183. https://doi.org/10.3390/bios14040183
Chicago/Turabian StyleIslam, Tanvir, and Peter Washington. 2024. "Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review" Biosensors 14, no. 4: 183. https://doi.org/10.3390/bios14040183