Determining an Optimal Oxygen Saturation Target Range Based on Neonatal Maturity: Demonstration of a Decision Tree Analytic.

The utility of decision tree machine learning in exploring the interactions among the SpO2 target range, neonatal maturity, and oxemic-risk is demonstrated. METHODS: This observational study used 3 years of paired age-SpO2-PaO2 data from a neonatal ICU. The CHAID decision tree method was used to explore the interaction of postmenstrual age (PMA) on the risk of extreme arterial oxygen levels at six different potential SpO2 target ranges (88-92%, 89-93%, 90-94%, 91-95%, 92-96% and 93-97%). Risk was calculated using a severity-weighted average of arterial oxygen outside the normal range for neonates (50-80 mmHg). RESULTS: In total, 7500 paired data points within the potential target range envelope were analyzed. The two lowest target ranges were associated with the highest risk, and the ranges of 91-95% and 92-96% were associated with the lowest risk. There were shifts in the risk associated with PMA. All the target ranges showed the lowest risk at ≥42 weeks PMA. The lowest risk for preterm infants was within a target range of 92-96% with a PMA of ≤34 weeks. CONCLUSIONS: This study demonstrates the utility of decision tree analytics. These results suggest that SpO2 target ranges that are different from typical range might reduce morbidity and mortality. Further research, including prospective randomized trials, is warranted.

Parametric evaluation of deep brain stimulation parameter configurations for Parkinson's disease using a conformal wearable and wireless inertial sensor system and machine learning.

Deep brain stimulation enables highly specified patient-unique therapeutic intervention ameliorating the symptoms of Parkinson's disease. Inherent to the efficacy of deep brain stimulation is the acquisition of an optimal parameter configuration. Using conventional methods, the optimization process for tuning the deep brain stimulation system parameters can intrinsically induce strain on clinical resources. An advanced means of quantifying Parkinson's hand tremor and distinguishing between parameter settings would be highly beneficial. The conformal wearable and wireless inertial sensor system, such as the BioStamp nPoint, has a volumetric profile on the order of a bandage that readily enables convenient quantification of Parkinson's disease hand tremor. Furthermore, the BioStamp nPoint has been certified by the FDA as a 510(k) medical device for acquisition of medical grade data. Parametric variation of the amplitude parameter for deep brain stimulation can be quantified through the BioStamp nPoint conformal wearable and wireless inertial sensor system mounted to the dorsum of the hand. The acquired inertial sensor signal data can be wirelessly transmitted to a secure Cloud computing environment for post-processing. The quantified inertial sensor data for the parametric study of the effects of varying amplitude can be distinguished through machine learning classification. Software automation through Python can consolidate the inertial sensor data into a suitable feature set format. Using the multilayer perceptron neural network considerable machine learning classification accuracy is attained to distinguish multiple parametric settings of amplitude for deep brain stimulation, such as 4.0 mA, 2.5 mA, 1.0 mA, and 'Off' status representing a baseline. These findings constitute an advance toward the pathway of attaining real-time closed loop automated parameter configuration tuning for treatment of Parkinson's disease using deep brain stimulation.

Wireless gyroscope platform enabled by a portable media device for quantifying wobble board therapy.

The wobble board enables a therapy strategy for rehabilitation of the ankle foot complex. Quantification of therapy, such as through the use of a wobble board, can facilitate a therapist's acuity for advancing and optimizing the overall therapy strategy. The portable media device, such as an iPod, can be equipped with a software application to function as a wireless gyroscope platform. Integration of the wobble board with the portable media device functioning as a wireless gyroscope enables the potential for patient to therapist interaction through connectivity to the Internet. A patient can conduct wobble board therapy for the ankle foot complex from the convenient vantage point of a homebound setting with therapy data transmitted wirelessly as email attachments. The gyroscope signal of the wobble board therapy can be consolidated into a feature set for machine learning classification. Using a multilayer perceptron neural network considerable classification accuracy has been achieved for differentiating between a hemiplegic affected ankle and unaffected ankle while using a wobble board. The combination of machine learning, wireless systems, such as a portable media device functioning as a wireless gyroscope, and a conventional therapy device, such as a wobble board, are envisioned to advance the capability to optimally impact the rehabilitation experience.

Virtual Proprioception for eccentric training.

Wireless inertial sensors enable quantified feedback, which can be applied to evaluate the efficacy of therapy and rehabilitation. In particular eccentric training promotes a beneficial rehabilitation and strength training strategy. Virtual Proprioception for eccentric training applies real-time feedback from a wireless gyroscope platform enabled through a software application for a smartphone. Virtual Proprioception for eccentric training is applied to the eccentric phase of a biceps brachii strength training and contrasted to a biceps brachii strength training scenario without feedback. During the operation of Virtual Proprioception for eccentric training the intent is to not exceed a prescribed gyroscope signal threshold based on the real-time presentation of the gyroscope signal, in order to promote the eccentric aspect of the strength training endeavor. The experimental trial data is transmitted wireless through connectivity to the Internet as an email attachment for remote post-processing. A feature set is derived from the gyroscope signal for machine learning classification of the two scenarios of Virtual Proprioception real-time feedback for eccentric training and eccentric training without feedback. Considerable classification accuracy is achieved through the application of a multilayer perceptron neural network for distinguishing between the Virtual Proprioception real-time feedback for eccentric training and eccentric training without feedback.

Implementation of a smartphone as a wireless gyroscope platform for quantifying reduced arm swing in hemiplegie gait with machine learning classification by multilayer perceptron neural network.

Natural gait consists of synchronous and rhythmic patterns for both the lower and upper limb. People with hemiplegia can experience reduced arm swing, which can negatively impact the quality of gait. Wearable and wireless sensors, such as through a smartphone, have demonstrated the ability to quantify various features of gait. With a software application the smartphone (iPhone) can function as a wireless gyroscope platform capable of conveying a gyroscope signal recording as an email attachment by wireless connectivity to the Internet. The gyroscope signal recordings of the affected hemiplegic arm with reduced arm swing arm and the unaffected arm are post-processed into a feature set for machine learning. Using a multilayer perceptron neural network a considerable degree of classification accuracy is attained to distinguish between the affected hemiplegic arm with reduced arm swing arm and the unaffected arm.

Use of smartphones and portable media devices for quantifying human movement characteristics of gait, tendon reflex response, and Parkinson's disease hand tremor.

Smartphones and portable media devices are both equipped with sensor components, such as accelerometers. A software application enables these devices to function as a robust wireless accelerometer platform. The recorded accelerometer waveform can be transmitted wireless as an e-mail attachment through connectivity to the Internet. The implication of such devices as a wireless accelerometer platform is the experimental and post-processing locations can be placed anywhere in the world. Gait was quantified by mounting a smartphone or portable media device proximal to the lateral malleolus of the ankle joint. Attributes of the gait cycle were quantified with a considerable accuracy and reliability. The patellar tendon reflex response was quantified by using the device in tandem with a potential energy impact pendulum to evoke the patellar tendon reflex. The acceleration waveform maximum acceleration feature of the reflex response displayed considerable accuracy and reliability. By mounting the smartphone or portable media device to the dorsum of the hand through a glove, Parkinson's disease hand tremor was quantified and contrasted with significance to a non-Parkinson's disease steady hand control. With the methods advocated in this chapter, any aspect of human movement may be quantified through smartphones or portable media devices and post-processed anywhere in the world. These wearable devices are anticipated to substantially impact the biomedical and healthcare industry.

Implementation of a smartphone wireless accelerometer platform for establishing deep brain stimulation treatment efficacy of essential tremor with machine learning.

Essential tremor (ET) is a highly prevalent movement disorder. Patients with ET exhibit a complex progressive and disabling tremor, and medical management often fails. Deep brain stimulation (DBS) has been successfully applied to this disorder, however there has been no quantifiable way to measure tremor severity or treatment efficacy in this patient population. The quantified amelioration of kinetic tremor via DBS is herein demonstrated through the application of a smartphone (iPhone) as a wireless accelerometer platform. The recorded acceleration signal can be obtained at a setting of the subject's convenience and conveyed by wireless transmission through the Internet for post-processing anywhere in the world. Further post-processing of the acceleration signal can be classified through a machine learning application, such as the support vector machine. Preliminary application of deep brain stimulation with a smartphone for acquisition of a feature set and machine learning for classification has been successfully applied. The support vector machine achieved 100% classification between deep brain stimulation in `on' and `off' mode based on the recording of an accelerometer signal through a smartphone as a wireless accelerometer platform.

Implementation of machine learning for classifying prosthesis type through conventional gait analysis.

Current forecasts imply a significant increase in the quantity of lower limb amputations. Synergizing the capabilities of a conventional gait analysis system and machine learning facilitates the capacity to classify disparate types of transtibial prostheses. Automated classification of prosthesis type may eventually advance rehabilitative acuity for selecting an appropriate prosthesis for a given aspect of the rehabilitation process. The presented research utilized a force plate as a conventional gait analysis device to acquire a feature set for two types of prosthesis: passive Solid Ankle Cushioned Heel (SACH) and the iWalk BiOM powered prosthesis. The feature set consists of both temporal and kinetic data with respect to the force plate signal during stance. Intuitively a passive prosthesis and powered prosthesis generate distinctively different force plate recordings. A support vector machine, which is type of machine learning application, achieves 100% classification between a passive prosthesis and powered prosthesis regarding the feature set derived from force plate recordings.

Implementation of an iPod wireless accelerometer application using machine learning to classify disparity of hemiplegic and healthy patellar tendon reflex pair.

The characteristics of the patellar tendon reflex provide fundamental insight regarding the diagnosis of neurological status. Based on the features of the tendon reflex response, a clinician may establish preliminary perspective regarding the global condition of the nervous system. Current techniques for quantifying the observations of the reflex response involve the application of ordinal scales, requiring the expertise of a highly skilled clinician. However, the reliability of the ordinal scale approach is debatable. Highly skilled clinicians have even disputed the presence of asymmetric reflex pairs. An alternative strategy was the implementation of an iPod wireless accelerometer application to quantify the reflex response acceleration waveform. An application enabled the recording of the acceleration waveform and later wireless transmission as an email attachment by connectivity to the Internet. A potential energy impact pendulum enabled the patellar tendon reflex to be evoked in a predetermined and targeted manner. Three feature categories of the reflex response acceleration waveform (global parameters, temporal organization, and spectral features) were incorporated into machine learning to distinguish a subject's hemiplegic and healthy reflex pair. Machine learning attained perfect classification of the hemiplegic and healthy reflex pair. The research findings implicate the promise of machine learning for providing increased diagnostic acuity regarding the acceleration waveform of the tendon reflex response.

Simulation of a computational winding filament model with an exponential spring to represent titin.

The goal of developing high fidelity simulation of muscle force is of considerable interest for the biomedical community. Traditionally Hill models have been incorporated. However, feasible scope of the Hill model is inherently limited, especially in light of the growing relevance of muscle history dependence. History dependence is considered to be significant for motor control and stability. Attempts have been made to augment the Hill model to emulate history dependence. The titin winding filament model best elucidates history dependence of muscle force including force enhancement. The recent version of the titin winding filament model accounts for the functionality of titin through a pulley linked with the contractile element and a linear spring to represent the elastic properties of titin. A new and more realistic amendment to the winding filament model is incorporation of an exponential spring to characterize the elastic properties of titin. A sensitivity study as a function of the titin exponential spring constant is presented. Overall the amalgamation of the titin exponential spring to the winding filament model improves the respective force enhancement characteristics with a relatively more optimal exponential spring constant that provides a maximal averaged coefficient of determination.

Implementation of a smartphone as a wireless gyroscope application for the quantification of reflex response.

The patellar tendon reflex constitutes a fundamental aspect of the conventional neurological evaluation. Dysfunctional characteristics of the reflex response can augment the diagnostic acuity of a clinician for subsequent referral to more advanced medical resources. The capacity to quantify the reflex response while alleviating the growing strain on specialized medical resources is a topic of interest. The quantification of the tendon reflex response has been successfully demonstrated with considerable accuracy and consistency through using a potential energy impact pendulum attached to a reflex hammer for evoking the tendon reflex with a smartphone, such as an iPhone, application representing a wireless accelerometer platform to quantify reflex response. Another sensor integrated into the smartphone, such as an iPhone, is the gyroscope, which measures rate of angular rotation. A smartphone application enables wireless transmission through Internet connectivity of the gyroscope signal recording of the reflex response as an email attachment. The smartphone wireless gyroscope application demonstrates considerable accuracy and consistency for the quantification of the tendon reflex response.

Implementation of a smartphone for evaluating gait characteristics of a trans-tibial prosthesis.

Smartphone applications have been demonstrated for their capacity to measure gait in functionally autonomous environments beyond the limitations of a traditional gait laboratory. A software application enables the iPhone to function as a wireless accelerometer platform. The recorded acceleration of gait can be transmitted wirelessly as an email attachment through Internet connectivity. The objective of the research was to demonstrate the capacity of the smartphone to quantify gait features of a trans-tibial prosthesis. The iPhone a standard smartphone was mounted to the carbon fiber blade of the prosthesis through an adapter developed by a 3D printer. The application demonstrated considerable accuracy and reliability for the quantification of gait characteristics.

Implementation of an iPhone wireless accelerometer application for the quantification of reflex response.

The patellar tendon reflex represents an inherent aspect of the standard neurological evaluation. The features of the reflex response provide initial perspective regarding the status of the nervous system. An iPhone wireless accelerometer application integrated with a potential energy impact pendulum attached to a reflex hammer has been successfully developed, tested, and evaluated for quantifying the patellar tendon reflex. The iPhone functions as a wireless accelerometer platform. The wide coverage range of the iPhone enables the quantification of reflex response samples in rural and remote settings. The iPhone has the capacity to transmit the reflex response acceleration waveform by wireless transmission through email. Automated post-processing of the acceleration waveform provides feature extraction of the maximum acceleration of the reflex response ascertained after evoking the patellar tendon reflex. The iPhone wireless accelerometer application demonstrated the utility of the smartphone as a biomedical device, while providing accurate and consistent quantification of the reflex response.

Quantified reflex strategy using an iPod as a wireless accelerometer application.

A primary aspect of a neurological evaluation is the deep tendon reflex, frequently observed through the patellar tendon reflex. The reflex response provides preliminary insight as to the status of the nervous system. A quantified reflex strategy has been developed, tested, and evaluated though the use of an iPod as a wireless accelerometer application integrated with a potential energy device to evoke the patellar tendon reflex. The iPod functions as a wireless accelerometer equipped with robust software, data storage, and the capacity to transmit the recorded accelerometer waveform of the reflex response wirelessly through email for post-processing. The primary feature of the reflex response acceleration waveform is the maximum acceleration achieved subsequent to evoking the patellar tendon reflex. The quantified reflex strategy using an iPod as a wireless accelerometer application yields accurate and consistent quantification of the reflex response.

Wireless accelerometer iPod application for quantifying gait characteristics.

The capability to quantify gait characteristics through a wireless accelerometer iPod application in an effectively autonomous environment may alleviate the progressive strain on highly specific medical resources. The iPod consists of the inherent attributes imperative for robust gait quantification, such as a three dimensional accelerometer, data storage, flexible software, and the capacity for wireless transmission of the gait data through email. Based on the synthesis of the integral components of the iPod, a wireless accelerometer iPod application for quantifying gait characteristics has been tested and evaluated in an essentially autonomous environment. The quantified gait acceleration waveforms were wirelessly transmitted using email for postprocessing. The site for the gait experiment occurred in a remote location relative to the location where the postprocessing was conducted. The wireless accelerometer iPod application for quantifying gait characteristics demonstrated sufficient accuracy and consistency.

Implementation of an iPhone for characterizing Parkinson's disease tremor through a wireless accelerometer application.

Parkinson's disease represents a chronic movement disorder, which is generally proportionally to age. The status of Parkinson's disease is traditionally classified through ordinal scale strategies, such as the Unified Parkinson's Disease Rating Scale. However, the application of the ordinal scale strategy inherently requires highly specialized and limited medical resources for interpretation. An alternative strategy involves the implementation of an iPhone application that enables the device to serve as a functional wireless accelerometer system. The Parkinson's disease tremor attributes may be recorded in either an effectively autonomous public or private setting, for which the resultant accelerometer signal of the tremor can be conveyed wireless and through email to a remote location for data post-processing. The initial testing and evaluation of the iPhone wireless accelerometer application for quantifying Parkinson's disease tremor successfully demonstrates the capacity to acquire tremor characteristics in an effectively autonomous environment, while potentially alleviating strain on limited and highly specialized medical resources.

Implementation of an iPhone as a wireless accelerometer for quantifying gait characteristics.

The capacity to quantify and evaluate gait beyond the general confines of a clinical environment under effectively autonomous conditions may alleviate rampant strain on limited and highly specialized medical resources. An iPhone consists of a three dimensional accelerometer subsystem with highly robust and scalable software applications. With the synthesis of the integral iPhone features, an iPhone application, which constitutes a wireless accelerometer system for gait quantification and analysis, has been tested and evaluated in an autonomous environment. The acquired gait cycle data was transmitted wireless and through email for subsequent post-processing in a location remote to the location where the experiment was conducted. The iPhone application functioning as a wireless accelerometer for the acquisition of gait characteristics has demonstrated sufficient accuracy and consistency.

Wireless accelerometer reflex quantification system characterizing response and latency.

The evaluation of the deep tendon reflex is a standard aspect of a neurological evaluation, which is frequently evoked through the patellar tendon reflex. Important features of the reflex are response and latency, providing insight to status for peripheral neuropathy and upper motor neuron syndrome. A wireless accelerometer reflex quantification system has been developed, tested, and evaluated. The reflex input is derived from a potential energy setting. Wireless accelerometers characterize the reflex hammer strike and reflex response acceleration waveforms, enabling the quantification of reflex response and latency. Spectral analysis of the reflex response acceleration waveform elucidates the frequency domain, opening the potential for new reflex classification metrics. The wireless accelerometer reflex quantification system yields accurate and consistent quantification of reflex response and latency.

Virtual proprioception with real-time step detection and processing.

Virtual proprioception is a novel device for providing near autonomous biofeedback of hemiparetic gait disparity in real time. With virtual proprioception a user may modify gait dynamics to develop a more suitable gait in tandem with real time feedback. Accelerometers are fundamental to the operation of the device, and a thorough consideration of the accelerometry technology space for locomotion quantification is included. The role of traumatic brain injury and respective decrements to gait quality and proprioceptive feedback are addressed. Virtual proprioception conceptual test and evaluation yielded positive results. The active 'on' status of the virtual proprioception biofeedback for alternative gait strategy was bounded by a 90% confidence level with a 10% margin of error.