A Spatiotemporal Deep Learning Framework for Scalp EEG-based Automated Pain Assessment in Children.

OBJECTIVE: Common pain assessment approaches such as self-evaluation and observation scales are inappropriate for children as they require patients to have reasonable communication ability. Subjective, inconsistent, and discontinuous pain assessment in children may reduce therapeutic effectiveness and thus affect their later life. METHODS: To address the need for suitable assessment measures, this paper proposes a spatiotemporal deep learning framework for scalp electroencephalogram (EEG)-based automated pain assessment in children. The dataset comprises scalp EEG data recorded from 33 pediatric patients with an arterial puncture as a pain stimulus. Two electrode reduction plans in line with clinical findings are proposed. Combining three-dimensional hand-crafted features and preprocessed raw signals, the proposed transformer-based pain assessment network (STPA-Net) integrates both spatial and temporal information. RESULTS: STPA-Net achieves superior performance with a subject-independent accuracy of 87.83% for pain recognition, and outperforms other state-of-the-art approaches. The effectiveness of electrode combinations is explored to analyze pain-related cortical activities and correspondingly reduce cost. The two proposed electrode reduction plans both demonstrate competitive pain assessment performance qualitatively and quantitatively. CONCLUSION AND SIGNIFICANCE: This study is the first to develop a scalp EEG-based automated pain assessment for children adopting a method that is objective, standardized, and consistent. The findings provide a potential reference for future clinical research.

Video-Based Neonatal Pain Assessment in Uncontrolled Conditions.

OBJECTIVE: Neonatal pain can have long-term adverse effects on newborns' cognitive and neurological development. Video-based Neonatal Pain Assessment (NPA) method has gained increasing attention due to its performance and practicality. However, existing methods focus on assessment under controlled environments while ignoring real-life disturbances present in uncontrolled conditions. METHODS: We propose a video-based NPA method, which is robust to four real-life disturbances and adaptively highlights keyframes. Our method involves a region-channel-attention module for extracting facial features under the disturbances of facial occlusion and pose variation; a body language analysis module robust to disturbances from body occlusion and movement interference, which utilizes skeleton sequences to represent the neonate's body; and a keyframes-aware convolution to get rid of information located at non-contributing moments. For evaluation, we built an NPA video dataset of 1091 neonates with disturbance annotations. RESULTS: The results show that our method consistently outperforms state-of-the-art methods on the full dataset and nine subsets, where it achieves an accuracy of 91.04% on the full dataset with an accuracy increment of 6.27%. Contributions: We present the problem of video-based NPA under uncontrolled conditions, propose a method robust to four disturbances, and construct a video NPA dataset, thus facilitating the practical applications of NPA.

Evaluation of procedural pain for neonates in a neonatal intensive care unit: a single-centre study.

BACKGROUND: To evaluate the procedural pain experienced by neonates in a neonatal intensive care unit (NICU) setting and determine the corresponding pain grades. METHODS: Two experienced nurses independently used the Neonatal Infant Pain Scale (NIPS) to evaluate the neonatal pain during procedures taking place in the tertiary NICU and two level-two neonatal care units in the Children's Hospital of Zhejiang University School of Medicine. The mean and distribution of NIPS pain scores and the corresponding pain grades of participants when experiencing clinical painful procedures were analysed. RESULTS: A total of 957 neonates exposed to 15 common clinical painful procedures were included in the study. The clinical painful procedures experienced by 957 participants could be divided into three groups: severe pain (NIPS score 5-7: peripheral intravenous cannulation, arterial catheterisation, arterial blood sampling, peripherally inserted central catheter placement and nasopharyngeal suctioning), mild to moderate pain (NIPS score 3-4: finger prick, intramuscular injection, adhesive removal, endotracheal intubation suctioning, heel prick, lumbar puncture and subcutaneous injection) and no pain to mild pain (NIPS score 0-2: gastric tube insertion, enema and intravenous injection). CONCLUSIONS: The neonatal pain response to clinical procedures in NICU had certain pattern and preintervention drug analgesia could be taken for painful procedures with clustered high NIPS pain scores. Meanwhile, full coverage non-drug pain relief measures could be taken for procedures that are with scattered pain scores, and real-time pain evaluation should be provided to determine whether further drug analgesia is required.

Pose-invariant and occlusion-robust neonatal facial pain assessment.

Neonatal Facial Pain Assessment (NFPA) is essential to improve neonatal pain management. Pose variation and occlusion, which can significantly alter the facial appearance, are two major and still unstudied barriers to NFPA. We bridge this gap in terms of method and dataset. Techniques to tackle both challenges in other tasks either expect pose/occlusion-invariant deep learning methods or first generate a normal version of the input image before feature extraction, combining these we argue that it is more effective to jointly perform adversarial learning and end-to-end classification for their mutual benefit. To this end, we propose a Pose-invariant Occlusion-robust Pain Assessment (POPA) framework, with two novelties. We incorporate adversarial learning-based disturbance mitigation for end-to-end pain-level classification and propose a novel composite loss function for facial representation learning; compared to the vanilla discriminator that implicitly determines occlusion and pose conditions, we propose a multi-scale discriminator that determines explicitly, while incorporating local discriminators to enhance the discrimination of key regions. For a comprehensive evaluation, we built the first neonatal pain dataset with disturbance annotation involving 1091 neonates and also applied the proposed POPA to the facial expression recognition task. Extensive qualitative and quantitative experiments prove the superiority of the POPA.

A Wearable Multi-Segment Upper Limb Tremor Assessment System for Differential Diagnosis of Parkinson's Disease Versus Essential Tremor.

Upper limb tremor is a prominent symptom of both Parkinson's disease and essential tremor. Its kinematic parameters overlap substantially for these two pathological conditions, thus leading to high rate of misdiagnosis, especially for community doctors. Several groups have proposed various methods for improving differential diagnosis. These prior studies have attempted to identify better kinematic parameters, however they have mainly focused on single limb features including tremor intensity, tremor frequency, and tremor variability. In this paper, we propose a wearable system for multi-segment assessment of upper limb tremor and differential diagnosis of Parkinson's disease versus essential tremor. The proposed system collected tremor data from both wrist and fingers simultaneously. From this data, we extracted multi-segment features in the form of phase relationships between limb segments. Using support vector machine classifiers, we then performed differential diagnosis from the extracted features. We evaluated the performance of the proposed system on 19 Parkinson's disease patients and 12 essential tremor patients. Moreover, we also assessed the performance cost associated with reducing task load and sensor array size. The proposed system reached perfect accuracy in leave-one-out cross validation. Task reduction and sensor array reduction were associated with penalties of 2% and 9-10% respectively. The results demonstrated that the proposed system could be simplified for clinical applications, and successfully applied to the differential diagnosis of Parkinson's disease versus essential tremor in real-world setting.

Video-Based versus On-Site Neonatal Pain Assessment in Neonatal Intensive Care Units: The Impact of Video-Based Neonatal Pain Assessment in Real-World Scenario on Pain Diagnosis and Its Artificial Intelligence Application.

BACKGROUND: Neonatal pain assessment (NPA) represents a huge global problem of essential importance, as a timely and accurate assessment of neonatal pain is indispensable for implementing pain management. PURPOSE: To investigate the consistency of pain scores derived through video-based NPA (VB-NPA) and on-site NPA (OS-NPA), providing the scientific foundation and feasibility of adopting VB-NPA results in a real-world scenario as the gold standard for neonatal pain in clinical studies and labels for artificial intelligence (AI)-based NPA (AI-NPA) applications. SETTING: A total of 598 neonates were recruited from a pediatric hospital in China. METHODS: This observational study recorded 598 neonates who underwent one of 10 painful procedures, including arterial blood sampling, heel blood sampling, fingertip blood sampling, intravenous injection, subcutaneous injection, peripheral intravenous cannulation, nasopharyngeal suctioning, retention enema, adhesive removal, and wound dressing. Two experienced nurses performed OS-NPA and VB-NPA at a 10-day interval through double-blind scoring using the Neonatal Infant Pain Scale to evaluate the pain level of the neonates. Intra-rater and inter-rater reliability were calculated and analyzed, and a paired samples t-test was used to explore the bias and consistency of the assessors' pain scores derived through OS-NPA and VB-NPA. The impact of different label sources was evaluated using three state-of-the-art AI methods trained with labels given by OS-NPA and VB-NPA, respectively. RESULTS: The intra-rater reliability of the same assessor was 0.976-0.983 across different times, as measured by the intraclass correlation coefficient. The inter-rater reliability was 0.983 for single measures and 0.992 for average measures. No significant differences were observed between the OS-NPA scores and the assessment of an independent VB-NPA assessor. The different label sources only caused a limited accuracy loss of 0.022-0.044 for the three AI methods. CONCLUSION: VB-NPA in a real-world scenario is an effective way to assess neonatal pain due to its high intra-rater and inter-rater reliability compared to OS-NPA and could be used for the labeling of large-scale NPA video databases for clinical studies and AI training.

A Three-Dimensional Finger-Tapping Framework for Recognition of Patients With Mild Parkinson's Disease.

The finger tapping test is a widely-used and important examination in the Movement Disorder Society Clinical Diagnosis for Parkinson's Disease. However, finger tapping motion could be affected by age, medication, and other conditions. As a result, Parkinson's disease patients with mild sign and healthy people could be rated as similar scores on the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale, making it difficult for community doctors to perform diagnosis. We therefore propose a three-dimensional finger tapping framework to recognize mild PD patients. Specifically, we first derive the three-dimensional finger-tapping motion using a self-designed three-dimensional finger-tapping measurement system. We then propose a three-dimensional finger-tapping segmentation algorithm to segment three-dimensional finger tapping motion. We next extract three-dimensional pattern features of motor coordination, imbalance impairment, and entropy. We finally adopted the support vector machine as the classifier to recognize PD patients. We evaluated the proposed framework on 49 PD patients and 29 healthy controls and reached an accuracy of 94.9% for the right hand and 89.4% for the left hand. Moreover, the proposed framework reached an accuracy of 95.0% for the right hand and 97.8% for the left hand on 17 mild PD patients and 28 healthy controls who were both rated as 0 or 1 on the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale. The results demonstrated that the proposed framework was less sensitive to traditional features and performed well in recognizing mild PD patients by involving three-dimensional patter features.

A Wearable Swallowing Recognition System Based on Motion and Dual Photoplethysmography Sensing of Laryngeal Movements.

Swallowing recognition is the leading step in the evaluation of dysphagia which seriously affects people's life. Current medical swallowing monitoring methods require an in-hospital environment and overly rely on professional knowledge of the medical staff. In this study, we developed a wearable swallowing recognition system that consists of an on-neck wearable swallowing sensing device and a data processing module on a host computer. The wearable device collects inertial signals including acceleration and angular velocity, as well as dual photoplethysmography (PPG) signals based on infrared and green light from the neck. A novel processing framework for dual PPG signals is proposed to extract and enhance the laryngeal motion component introduced by swallowing activities in the data processing module. The laryngeal motion component of dual PPG signals together with the preprocessed inertial signals are further used for feature extraction to proceed swallowing recognition based on random forest classifier. We collected data from 32 healthy subjects in the center and side positions on the neck using our system to analyze their swallowing activities. As a result, we achieved a high average area under curve (AUC) of the swallowing recognition by 86.6%. We also find the sensing position has a significant impact on gender-specific swallowing recognition performance, as the center position was better for females (92.9%), while the side position was better for males (87.6%). The results indicate that the proposed system could achieve high integrity and good performance, which is helpful for the future swallowing research.

Artificial Intelligence Based Pain Assessment Technology in Clinical Application of Real-World Neonatal Blood Sampling.

Background: Accurate neonatal pain assessment (NPA) is the key to neonatal pain management, yet it is a challenging task for medical staff. This study aimed to analyze the clinical practicability of the artificial intelligence based NPA (AI-NPA) tool for real-world blood sampling. Method: We performed a prospective study to analyze the consistency of the NPA results given by a self-developed automated NPA system and nurses' on-site NPAs (OS-NPAs) for 232 newborns during blood sampling in neonatal wards, where the neonatal infant pain scale (NIPS) was used for evaluation. Spearman correlation analysis and the degree of agreement of the pain score and pain grade derived by the NIPS were applied for statistical analysis. Results: Taking the OS-NPA results as the gold standard, the accuracies of the NIPS pain score and pain grade given by the automated NPA system were 88.79% and 95.25%, with kappa values of 0.92 and 0.90 (p < 0.001), respectively. Conclusion: The results of the automated NPA system for real-world neonatal blood sampling are highly consistent with the results of the OS-NPA. Considering the great advantages of automated NPA systems in repeatability, efficiency, and cost, it is worth popularizing the AI technique in NPA for precise and efficient neonatal pain management.

Robust Heartbeat Classification for Wearable Single-Lead ECG via Extreme Gradient Boosting.

Wearable electrocardiogram (ECG) monitoring devices have enabled everyday ECG collection in our daily lives. However, the condition of ECG signal acquisition using wearable devices varies and wearable ECG signals could be interfered with by severe noises, resulting in great challenges of computer-aided automated ECG analysis, especially for single-lead ECG signals without spare channels as references. There remains room for improvement of the beat-level single-lead ECG diagnosis regarding accuracy and efficiency. In this paper, we propose new morphological features of heartbeats for an extreme gradient boosting-based beat-level ECG analysis method to carry out the five-class heartbeat classification according to the Association for the Advancement of Medical Instrumentation standard. The MIT-BIH Arrhythmia Database (MITDB) and a self-collected wearable single-lead ECG dataset are used for performance evaluation in the static and wearable ECG monitoring conditions, respectively. The results show that our method outperforms other state-of-the-art models with an accuracy of 99.14% on the MITDB and maintains robustness with an accuracy of 98.68% in the wearable single-lead ECG analysis.

Three-Dimensional Pattern Features in Finger Tapping Test for Patients with Parkinson's disease.

Finger tapping test is an important neuropsychological test to evaluate human motor function. Most recent researches simplified the finger tapping motion as a scissors-like motion, though the rotation axis of the thumb was different from that of the forefinger. In this paper, we proposed a three-dimensional (3-D) finger tapping measurement system to obtain 3-D pattern features in finger tapping test for patients with Parkinson's disease (PD). The proposed system collected the motion of the thumb and the forefinger by nine-degrees-freedom sensors and calculated 3-D motion of finger tapping by an orientation estimation method and a 3-D finger-tapping kinematic model. We further extracted 3-D pattern features, i.e. motor coordination and relative thumb motion, from 3-D Finger Tapping motion. Moreover, we used the proposed system to collect the finger-tapping motion of 43 PD patients and 30 healthy controls in horizontal tasks and vertical tasks. The results indicated that 3-D pattern features showed a better performance than one-dimensional features in the identification of mild PD patients.Clinical Relevance- These three-dimensional pattern features could be used to evaluate finger tapping motion in a novel way, which could be used to better identify mild Parkinson's disease patients. Furthermore, the results showed that a combination of horizontal tasks and vertical tasks might be a better way to identify mild Parkinson's disease patients.

Optimized Electrode Locations for Wearable Single-Lead ECG Monitoring Devices: A Case Study Using WFEES Modules based on the LANS Method.

Body surface potential mapping (BSPM) is a valuable tool for research regarding electrocardiograms (ECG). However, the BSPM system is limited by its large number of electrodes and wires, long installation time, and high computational complexity. In this paper, we designed a wearable four-electrode electrocardiogram-sensor (WFEES) module that measures six-channel ECGs simultaneously for ECG investigation. To reduce the testing lead number and the measurement complexity, we further proposed a method, the layered (A, N) square-based (LANS) method, to optimize the ECG acquisition and analysis process using WFEES modules for different applications. Moreover, we presented a case study of electrode location optimization for wearable single-lead ECG monitoring devices using WFEES modules with the LANS method. In this study, 102 sets of single-lead ECG data from 19 healthy subjects were analyzed. The signal-to-noise ratio of ECG, as well as the mean and coefficient of variation of QRS amplitude, was derived among different channels to determine the optimal electrode locations. The results showed that a single-lead electrode pair should be placed on the left chest above the electrode location of standard precordial leads V1 to V4. Additionally, the best orientation was the principal diagonal as the direction of the heart's electrical axis.

A lightweight piecewise linear synthesis method for standard 12-lead ECG signals based on adaptive region segmentation.

This paper presents a lightweight synthesis algorithm, named adaptive region segmentation based piecewise linear (ARSPL) algorithm, for reconstructing standard 12-lead electrocardiogram (ECG) signals from a 3-lead subset (I, II and V2). Such a lightweight algorithm is particularly suitable for healthcare mobile devices with limited resources for computing, communication and data storage. After detection of R-peaks, the ECGs are segmented by cardiac cycles. Each cycle is further divided into four regions according to different cardiac electrical activity stages. A personalized linear regression algorithm is then applied to these regions respectively for improved ECG synthesis. The proposed ARSPL method has been tested on 39 subjects randomly selected from the PTB diagnostic ECG database and achieved accurate synthesis of remaining leads with an average correlation coefficient of 0.947, an average root-mean-square error of 55.4µV, and an average runtime performance of 114ms. Overall, these results are significantly better than those of the common linear regression method, the back propagation (BP) neural network and the BP optimized using the genetic algorithm. We have also used the reconstructed ECG signals to evaluate the denivelation of ST segment, which is a potential symptom of intrinsic myocardial disease. After ARSPL, only 10.71% of the synthesized ECG cycles are with a ST-level synthesis error larger than 0.1mV, which is also better than those of the three above-mentioned methods.

[Prognostic factors for elderly patients with pancreaticoduodenectomy for periampullary tumor].

OBJECTIVES: To analyze the prognostic factors for elderly patients with pancreaticoduodenectomy for periampullary tumor. METHODS: A retrospective analysis of the prognostic factors for the mortality rate was made in 127 elderly patients within 30 days of pancreaticoduodenectomy for periampullary tumor from January 1985 to November 2006 Chi-squared test, Fisher's exact test, t-test were used. RESULTS: The prognostic factors for the first-month mortality rate in elderly patients with pancreaticoduodenectomy included time length of the operation, operative hemorrhage, postoperative hemorrhage, pulmonary infection, and postoperative TP. CONCLUSIONS: An overall consideration should be paid to the factors that affect the prognosis of elderly patients with pancreaticoduodenectomy for periampullary tumor during the perioperative period. The security of the patients can be promoted by controlling these prognostic factors.