Profile of Obstetric Patients in Intensive Care Unit: A Retrospective Study from a Tertiary Care Center in North India.

BACKGROUND: Critically ill obstetric patients constitute a small number of intensive care unit (ICU) admissions. Physiological changes in pregnancy along with certain pregnancy-specific diseases may cause a rapid worsening of the health status of the patient necessitating ICU care. The present study aims to study the clinical profile of the obstetric patients requiring ICU care. MATERIALS AND METHODS: It was a retrospective analysis of pregnant/postpartum (up to 6 weeks) admissions over a period of 18 months. RESULTS: Over these 18 months, 127 women required ICU admission. The most common reasons for ICU admission were obstetric hemorrhage (37.79%) and (pre)eclampsia (28.35%). Ten patients presented with antepartum hemorrhage (placenta previa, placenta accreta, placenta increta). The rest of the patients (n = 38) had atonic postpartum hemorrhage with five having severe anemia. Among the nonobstetric causes (n = 26/127), ICU admission was the most common among those with preexisting heart diseases (n = 10; 7.87%). Forty-nine patients were ventilated mechanically (38.58%), with eclampsia being the most common primary diagnosis (n = 23). We observed 10 maternal deaths (7.87%) with septicemia being the most important cause of death. CONCLUSIONS: Maternal and child health has become an important measure of human and social development. Early diagnosis and prompt treatment of high-risk obstetric patients in a dedicated obstetric ICU in tertiary hospitals can prevent severe maternal morbidity and improve maternal care. HOW TO CITE THIS ARTICLE: Gupta H, Gandotra N, Mahajan R. Profile of Obstetric Patients in Intensive Care Unit: A Retrospective Study from a Tertiary Care Center in North India. Indian J Crit Care Med 2021;25(4):388-391.

Comparative analysis between convolutional neural network learned and engineered features: A case study on cardiac arrhythmia detection.

Background: Atrial fibrillation (AF) is one of the most common cardiovascular problems, and its asymptomatic tendency makes AF detection challenging. Machine and deep learning methods are commonly used in AF detection. Objective: The purpose of this study was to evaluate the information provided by convolutional neural network (CNN) and random forest (RF) machine learning models for AF classification. Methods: We manually extracted 166 time-frequency domains and linear and nonlinear features to classify single-lead electrocardiograms (ECGs) as normal, AF, other, or noisy sinus rhythms. We selected a subset of 56 robust features using a genetic algorithm that was used in the RF model. In a separate study, a 1-dimensional, 12-layer CNN was designed on the raw ECG rhythms. Four features from the output layer and 128 features from the fully connected layer of CNN were explored independently for classification. The models were trained and internally validated on 8,528 ECGs and externally validated on a hidden dataset containing 3,658 ECGs. Next,we analyzed the correlation between engineered and CNN-learned features. Results: An RF classifier trained with 56-engineered features resulted in an F1 score of 0.91, 0.78, and 0.72 for normal, AF, and other rhythms, respectively. However, an ensemble of support vector machine and the CNN model resulted in an F1 score of 0.92, 0.87, and 0.80, respectively. Conclusion: We explored various features and machine learning models to identify AF rhythms using short (9-61 seconds) single-lead ECG recordings. Our results showed that the proposed CNN model abstracted distinctive features for AF classification.

PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform.

Real-time analysis of streaming physiological data to identify earlier abnormal conditions is an important aspect of precision medicine. However, open-source systems supporting this workflow are lacking. In this paper, we present PhysOnline, a pipeline built on the open-source Apache Spark platform to ingest streaming physiological data for online feature extraction and machine learning. We consider scalability factors for horizontal deployment to support growing analysis requirements. We further integrate real-time feature extraction, including pattern recognition methods as well as descriptive statistical components to identify temporal characteristics of waveform signals. These generated features are then used for machine learning and for real-time classification of abnormal conditions. As a case study, we present the online classification of electrocardiography recordings for screening Paroxysmal Atrial Fibrillation (PAF) and demonstrate that our pipeline can predict persons developing PAF at least 45 min. before an episode of that condition. This pipeline can be applied in domains where pattern matching, temporal abstractions, and morphological characteristics can be used for real-time classification of streaming time-series data.1.

Disparities in Population-Level Socio-Economic Factors Are Associated with Disparities in Preoperative Clinical Risk Factors in Children.

African American children are more than twice as likely as white American children to die after surgery, and have increased risk for longer hospital stays, post-surgical complications, and higher hospital costs. Prior research into disparities in pediatric surgery outcomes has not considered interactions between patient-level Clinical Risk Factors (CRFs) and population-level Social, Economic, and Environmental Factors (SEEFs) primarily due to the lack of integrated data sets. In this study, we analyze correlations between SEEFs and CRFs and correlations between CRFs and surgery outcomes. We used a dataset from a cohort of 460 surgical cases who underwent surgery at a children's hospital in Memphis, Tennessee in the United States. The analysis was conducted on 23 CRFs, 9 surgery outcomes, and 10 SEEFs and demographic variables. Our results show that population-level SEEFs are significantly associated with both patient-level CRFs and surgery outcomes. These findings may be important in the improved understanding of health disparities in pediatric surgery outcomes.

BRAINsens: Body-Worn Reconfigurable Architecture of Integrated Network Sensors.

Body sensor network (BSN) is a promising human-centric technology to monitor neurophysiological data. We propose a fully-reconfigurable architecture that addresses the major challenges of a heterogenous BSN, such as scalabiliy, modularity and flexibility in deployment. Existing BSNs especially with Electroencephalogarm (EEG) have these limitations mainly due to the use of driven-right-leg (DRL) circuit. We address these limitations by custom-designing DRL-less EEG smart sensing nodes (SSN) for modular and spatially distributed systems. Each single-channel EEG SSN with a input-referred noise of 0.82 µVrms and CMRR of 70 dB (at 60 Hz), samples brain signals at 512 sps. SSNs in the network can be configured at the time of deployment and can process information locally to significantly reduce data payload of the network. A Control Command Node (CCN) initializes, synchronizes, periodically scans for the available SSNs in the network, aggregates their data and sends it wirelessly to a paired device at a baud rate of 115.2 kbps. At the given settings of the I2C bus speed of 100 kbps, CCN can configure up to 39 EEG SSNs in a lego-like platform. The temporal and frequency-domain performance of the designed "DRL-less" EEG SSNs is evaluated against a research-grade Neuroscan and consumer-grade Emotiv EPOC EEG. The results show that the proposed network system with wearable EEG can be deployed in situ for continuous brain signal recording in real-life scenarios. The proposed system can also seamlessly incorporate other physiological SSNs for ECG, HRV, temperature etc. along with EEG within the same topology.

A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length.

OBJECTIVE: Atrial fibrillation (AF) is a major cause of hospitalization and death in the United States. Moreover, as the average age of individuals increases around the world, early detection and diagnosis of AF become even more pressing. In this paper, we introduce a novel deep learning architecture for the detection of normal sinus rhythm, AF, other abnormal rhythms, and noise. APPROACH: We have demonstrated through a systematic approach many hyperparameters, input sets, and optimization methods that yielded influence in both training time and performance accuracy. We have focused on these properties to identify an optimal 13-layer convolutional neural network (CNN) model which was trained on 8528 short single-lead ECG recordings and evaluated on a test dataset of 3658 recordings. MAIN RESULTS: The proposed CNN architecture achieved a state-of-the-art performance in identifying normal, AF and other rhythms with an average F 1-score of 0.83. SIGNIFICANCE: We have presented a robust deep learning-based architecture that can identify abnormal cardiac rhythms using short single-lead ECG recordings. The proposed architecture is computationally fast and can also be used in real-time cardiac arrhythmia detection applications.

Sociomarkers and biomarkers: predictive modeling in identifying pediatric asthma patients at risk of hospital revisits.

The importance of social components of health has been emphasized both in epidemiology and public health. This paper highlights the significant impact of social components on health outcomes in a novel way. Introducing the concept of sociomarkers, which are measurable indicators of social conditions in which a patient is embedded, we employed a machine learning approach that uses both biomarkers and sociomarkers to identify asthma patients at risk of a hospital revisit after an initial visit with an accuracy of 66%. The analysis has been performed over an integrated dataset consisting of individual-level patient information such as gender, race, insurance type, and age, along with ZIP code-level sociomarkers such as poverty level, blight prevalence, and housing quality. Using this uniquely integrated database, we then compare the traditional biomarker-based risk model and the sociomarker-based risk model. A biomarker-based predictive model yields an accuracy of 65% and the sociomarker-based model predicts with an accuracy of 61%. Without knowing specific symptom-related features, the sociomarker-based model can correctly predict two out of three patients at risk. We systematically show that sociomarkers play an important role in predicting health outcomes at the individual level in pediatric asthma cases. Additionally, by merging multiple data sources with detailed neighborhood-level data, we directly measure the importance of residential conditions for predicting individual health outcomes.

Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics.

OBJECTIVE: A timely diagnosis of congestive heart failure (CHF) is crucial to evade a life-threatening event. This paper presents a novel probabilistic symbol pattern recognition (PSPR) approach to detect CHF in subjects from their cardiac interbeat (R-R) intervals. METHOD: PSPR discretizes each continuous R-R interval time series by mapping them onto an eight-symbol alphabet and then models the pattern transition behavior in the symbolic representation of the series. The PSPR-based analysis of the discretized series from 107 subjects (69 normal and 38 CHF subjects) yielded discernible features to distinguish normal subjects and subjects with CHF. In addition to PSPR features, we also extracted features using the time-domain heart rate variability measures such as average and standard deviation of R-R intervals. RESULTS: An ensemble of bagged decision trees was used to classify two groups resulting in a five-fold cross-validation accuracy, specificity, and sensitivity of 98.1%, 100%, and 94.7%, respectively. However, a 20% holdout validation yielded an accuracy, specificity, and sensitivity of 99.5%, 100%, and 98.57%, respectively. Results from this study suggest that features obtained with the combination of PSPR and long-term heart rate variability measures can be used in developing automated CHF diagnosis tools.

Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data.

Electroencephalogram (EEG) is a technique for recording the asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. Artifacts, such as eye blink activities, can corrupt these neuronal signals. While ocular artifact (OA) removal is well investigated for multiple channel EEG systems, in alignment with the recent momentum toward minimalistic EEG systems for use in natural environments, we investigate unsupervised and effective removal of OA from single-channel streaming raw EEG data. In this paper, the unsupervised wavelet transform (WT) decomposition technique was systematically evaluated for the effectiveness of OA removal for a single-channel EEG system. A set of seven raw EEG data set was analyzed. Two commonly used WT methods, Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), were applied. Four WT basis functions, namely, haar, coif3, sym3, and bior4.4, were considered for OA removal with universal threshold and statistical threshold (ST). To quantify OA removal efficacy from single-channel EEG, five performance metrics were utilized: correlation coefficients, mutual information, signal-to-artifact ratio, normalized mean square error, and time-frequency analysis. The temporal and spectral analysis shows that the optimal combination could be DWT with ST with coif3 or bior4.4 to remove OA among 16 combinations. This paper demonstrates that the WT can be an effective tool for unsupervised OA removal from single-channel EEG data for real-time applications.

High-accuracy user identification using EEG biometrics.

We analyze brain waves acquired through a consumer-grade EEG device to investigate its capabilities for user identification and authentication. First, we show the statistical significance of the P300 component in event-related potential (ERP) data from 14-channel EEGs across 25 subjects. We then apply a variety of machine learning techniques, comparing the user identification performance of various different combinations of a dimensionality reduction technique followed by a classification algorithm. Experimental results show that an identification accuracy of 72% can be achieved using only a single 800 ms ERP epoch. In addition, we demonstrate that the user identification accuracy can be significantly improved to more than 96.7% by joint classification of multiple epochs.

Design and validation of a wearable "DRL-less" EEG using a novel fully-reconfigurable architecture.

The conventional EEG system consists of a driven-right-leg (DRL) circuit, which prohibits modularization of the system. We propose a Lego-like connectable fully reconfigurable architecture of wearable EEG that can be easily customized and deployed at naturalistic settings for collecting neurological data. We have designed a novel Analog Front End (AFE) that eliminates the need for DRL while maintaining a comparable signal quality of EEG. We have prototyped this AFE for a single channel EEG, referred to as Smart Sensing Node (SSN), that senses brain signals and sends it to a Command Control Node (CCN) via an I2C bus. The AFE of each SSN (referential-montage) consists of an off-the-shelf instrumentation amplifier (gain=26), an active notch filter fc = 60Hz), 2nd-order active Butterworth low-pass filter followed by a passive low pass filter (fc = 47.5 Hz, gain = 1.61) and a passive high pass filter fc = 0.16 Hz, gain = 0.83). The filtered signals are digitized using a low-power microcontroller (MSP430F5528) with a 12-bit ADC at 512 sps, and transmitted to the CCN every 1 s at a bus rate of 100 kbps. The CCN can further transmit this data wirelessly using Bluetooth to the paired computer at a baud rate of 115.2 kbps. We have compared temporal and frequency-domain EEG signals of our system with a research-grade EEG. Results show that the proposed reconfigurable EEG captures comparable signals, and is thus promising for practical routine neurological monitoring in non-clinical settings where a flexible number of EEG channels are needed.

Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, Kurtosis, and wavelet-ICA.

Brain activities commonly recorded using the electroencephalogram (EEG) are contaminated with ocular artifacts. These activities can be suppressed using a robust independent component analysis (ICA) tool, but its efficiency relies on manual intervention to accurately identify the independent artifactual components. In this paper, we present a new unsupervised, robust, and computationally fast statistical algorithm that uses modified multiscale sample entropy (mMSE) and Kurtosis to automatically identify the independent eye blink artifactual components, and subsequently denoise these components using biorthogonal wavelet decomposition. A 95% two-sided confidence interval of the mean is used to determine the threshold for Kurtosis and mMSE to identify the blink related components in the ICA decomposed data. The algorithm preserves the persistent neural activity in the independent components and removes only the artifactual activity. Results have shown improved performance in the reconstructed EEG signals using the proposed unsupervised algorithm in terms of mutual information, correlation coefficient, and spectral coherence in comparison with conventional zeroing-ICA and wavelet enhanced ICA artifact removal techniques. The algorithm achieves an average sensitivity of 90% and an average specificity of 98%, with average execution time for the datasets ( N = 7) of 0.06 s ( SD = 0.021) compared to the conventional wICA requiring 0.1078 s ( SD = 0.004). The proposed algorithm neither requires manual identification for artifactual components nor additional electrooculographic channel. The algorithm was tested for 12 channels, but might be useful for dense EEG systems.

Effect of BMI and urinary pH on urolithiasis and its composition.

Urolithiasis is a common urological disease predominantly affecting males. The lifetime risk of urolithiasis varies from 1% to 5% in Asia, 5% to 9% in Europe, 10% to 15% in the USA and 20% to 25% in the middle-east; lowest prevalence is reported from Greenland and Japan. Such differences have been explained on the basis of race, diet and climate factors. Furthermore, changing socio-economic conditions have generated changes in the prevalence, incidence and distribution for age, sex and type of lithiasis in terms of both the site and the chemical as well as the physical composition of the calculi. The aim of our study was to determine the association between body mass index (BMI) and urine pH in patients with urolithiasis and the influence of body size, as reflected by the BMI, on the composition. The study was conducted in the Department of Biochemistry, Maharishi Markandeshwar Institute of Medical Sciences and Research, on urolithiatic patients. The data included patient's age, sex, BMI, urine pH, serum calcium, serum uric acid, serum creatinine and stone composition. Data from 100 patients, 70 men (70%) and 30 women (30%), were analyzed, with 28 patients having normal weight, 38 patients being overweight and 34 patients being obese. The mean age of the patients was 36.58 ± 9.91 years in group I, 40.47 ± 14.48 years in group II and 37.85 ± 12.46 years in group III (P > 0.05). The stone composition was calcium oxalate (CaOx) in 66 patients, calcium phosphate (CaP) in 60 patients, uric acid (UA) in 38 patients, combined calcium oxalate and calcium phosphate in 28 patients and three stones in 10 patients. The urinary pH levels (mean ± SD) were 7.78 ± 1.49 in group I, 7.15 ± 1.11 in group II and 6.29 ± 1.14 in group III patients (P = 0.0001). Urine pH showed a stepwise decrease with increasing BMI (inverse correlation). Urine pH is inversely related to BMI among patients with urolithiasis, as is the occurrence of urate, calcium oxalate and calcium phosphate stones. Similarly, the serum creatinine increased as the BMI and number of stones increased among the study population.