CONTINUOUS PARKINSON’S DISEASE SYMPTOM MONITORING

Anastasios Manos

Business DirectorV1.0 APRIL 2019

OBJECTIVE MEASURING AND MONITORING ACTIVITY AND PD SYMPTOMS

Objective measuring of quantification and identification of activity and symptoms in PDpatients using devices, either through tests conducted by clinicians in controlledenvironments or through tasks performed by patients themselves at their homes orcommunity centers.

These device-based measures can be used to detect and quantify PD-related motor andnonmotor impairments in specific or overall function in everyday life [1].

[1] Espay, A. J., Bonato, P., Nahab, F. B., Maetzler, W., Dean, J. M., Klucken, J., et al, the Movement Disorders Society Task Force on Technology. Technology in Parkinson’s disease: Challenges and opportunities. Movement Disorders, 2016, 31(9), 1272-82.

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WHY?

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PARKINSON’S DISEASE (PD) IS THE 2ND MOST COMMON (FOLLOWING ALZHEIMER’S), WITH A SIGNIFICANT ECONOMIC BURDEN TO THE SOCIETY

Parkinson’s Disease

Patients Globally

2.5 mil*

The annual cost of the

disease***

14.0 €B

1990

8.5 mil*2017

>10.0 mil**2030

240%

[*] GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: asystematic analysis for the Global Burden of Disease Study 2017. Lancet Global Health Metrics 2018; 392(10159): 1789-1858.[**] Dorsey E R; Constantinescu R; Thompson J P; Biglan K M; Holloway R G; Kieburtz K; Marshall F J; Ravina B M; Schifitto G; Siderowf A; Tanner C M. Projected number of people with Parkinson's disease in the most populous nations,2005 through 2030. Neurology. 2007; 68(5): 384-386.[***] Kowal S L, Dall T M, Chakrabarti R, Storm M V, Jain A. The current and projected economic burden of Parkinson's disease in the United States. Mov. Disord. 2013; 28(3): 311-318.[***] EPDA. The European Parkinson’s Disease Standards of Care Consensus Statement. 2011.

EU

25.0 $B

USA

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OPTIMIZATION OF DOSE IS CRUCIAL, SINCE THE WAY PD IS MANAGED, IT DETERIORATES THE EFFICIENCY OF THE MEDICATION USED

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Drug administration when symptoms

worsen1

2

3

4

2

1 1 1

3 3

4

Disease progression

Symptoms are controlled

Non optimal dosing, side effects like

Dyskinesia

Disease progresses in time,

therapeutic window decreases (2 vs. 4)

WHY? THE NEED FOR TECHNOLOGY-BASED MEASURES IN LONG-TERM TREATMENT.

So far good results in the management of PD symptoms have been achieved, particularly in the earlystages of the disease [1, 2]. However, two major problems hamper long-term treatment [3]:

Medication

Current pharmacological therapy issuccessful for a limited period. In thelong term, most patients developunmanageable motor complicationsthat can lead to worsening of qualityof life.

Availability

Evaluation of day-to-day variations isdifficult when relying solely uponperiodic consultations. A visit to theclinic offers face to face contact, but is,at best, a very crude and ofteninaccurate perspective of the patient’sreal functioning at home [5].

Devices can capture objective day-to-day data and offer more detailed and reliable measures during daily living, improving the management of PD [4].

[1] Lewis S., Foltynie T., Blackwell A., Robbins T., Owen A., Barker R. Heterogeneity of Parkinson's disease in the early clinical stages using a data driven approach. Journal of Neurology, Neurosurgery & Psychiatry, 2005, 76(3), 343-8.[2] Giugni J.C., Okun M.S. Treatment of advanced Parkinson's disease. Current opinion in neurology, 2014, 27(4), 450-60. [3] Silva de Lima A.L., Hahn T., Evers L.J.W., de Vries N.M., Cohen E., Afek M., et al. Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease. PLoS ONE, 2017, 12(12), e0189161. [4] Ben-Pazi, H., Browne, P., Chan, P. et al. the International Parkinson and Movement Disorder Society Telemedicine Task Force. The Promise of Telemedicine for Movement Disorders: an Interdisciplinary Approach. Curr Neurol Neurosci Rep, 2018, 18, 26.

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2011

40%

In a US study, more than 40% of patients with PD did not receive any neurologist

care [7].

2030

100%As the global incidence and prevalence

of PD are expected to rise, doublingbetween 2005 and 2030 [8], the

shortage of neurologists is predicted to increase as well, widening the gap

between those in need of specialized PD care and those providing it [9].

Evidence shows that PDpatients who receivetreatment from a movementdisorders specialist showimproved health outcomes,greater adherence to qualityindicators and increasedpatient satisfaction [6].

However…

[1] Dorsey E.R., Voss T.S., Shprecher D.R., Deuel L.M., Beck C.A., Gardiner I.F., et al. A U.S. survey of patients with Parkinson’s disease: satisfaction with medical care and support groups. Mov Disord, 2010, 25, 2128-35.[2] Burke JF, Albin RL. Do neurologists make a difference in Parkinson disease care? Neurology, 2011, 77, e52–3.[3] Dorsey E.R., Bloem B.R. The Parkinson pandemic-a call to action. JAMA Neurol, 2018, 75, 9-10.[4] Dall T.M., Storm M.V., Chakrabarti R., Drogan O., Keran C.M., Donofrio P.D., et al. Supply and demand analysis of the current and future US neurology workforce. Neurology. 2013, 81, 470-8.

WHY? THE NEED FOR TECHNOLOGY-BASED MEASURES. SCARCITY OF SPECIALIZED CARE.

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Increased difficulty to access proper care.

Disease Progression

Mobility Limitations

1

2

3

Sparsity

Disease Progression

PD is a neurodegenerative disease. It only worsens withtime. Medication side-effects are very common after 3-5years.

Sparsity

Sparse distribution of Movement Disorders (MD) specialists.

Mobility Limitations

PD patients experience mobility limitations ranging frominconvenience to severe impairments.

Adapted from: The Promise of Telemedicine for Movement Disorders: an Interdisciplinary Approach [1].

“…can lead to unnecessary evaluations in theemergency room and at times even costlyinterventions, all of which have the unintendedconsequence of increasing health resourceutilization” [2].

[1] Ben-Pazi, H., Browne, P., Chan, P. et al. the International Parkinson and Movement Disorder Society Telemedicine Task Force. The Promise of Telemedicine for Movement Disorders: an Interdisciplinary Approach. Curr Neurol Neurosci Rep, 2018, 18, 26. [2] Papapetropoulos S., Mitsi G., Espay A.J. Digital Health revolution: is it time for affordable remote monitoring for Parkinson’s disease? Frontiers in Neurology, 2015, 6, 34.

THE NEED FOR TECHNOLOGY-BASED MEASURES.

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HOW?

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TECHNOLOGY-BASED MEASURING OF EVERYDAY FUNCTION

Feasibility studies for technology-based applications for remote measuring and monitoring of function and symptoms for PD patients have had positive outcomes [4, 5].

User-friendly technology-based instruments for measuring function and monitoring treatment-induced motor complications in the home setting could revolutionize access to care and enhance treatment optimization with currently available drugs [10].

A paradigm shift is required…

[4] Silva de Lima A.L., Hahn T., Evers L.J.W., de Vries N.M., Cohen E., Afek M., et al. Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease. PLoS ONE, 2017, 12(12), e0189161. [5] Ben-Pazi, H., Browne, P., Chan, P. et al. the International Parkinson and Movement Disorder Society Telemedicine Task Force. The Promise of Telemedicine for Movement Disorders: an Interdisciplinary Approach. Curr Neurol Neurosci Rep, 2018, 18, 26. [10] Papapetropoulos S., Mitsi G., Espay A.J. Digital Health revolution: is it time for affordable remote monitoring for Parkinson’s disease? Frontiers in Neurology, 2015, 6, 34.

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CURRENT DISCONNECTED PROCESS…

PatientSymptoms are

present and the patient needs care.

ReportsThe patient should be keeping a diary with the symptoms and their severity but as cognitive function becomes impaired and visits more sparse, classic patient diaries are not reliable.

ExaminationAfter a few months in most cases, a clinical

examination is scheduled. The symptoms could be absent during the visit. Depending

on the patient’s cognitive status, the testimony regarding the symptoms and

fluctuations can be unreliable. Also, depending on the area, the availability of a

specialized physician can not be considered certain.

TreatmentMedication titration will have to occur either due to ON-OFF fluctuations or Dyskinesia

R

E

T

P

Disease Progression

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…TO A PATIENT-CENTRIC COLLABORATIVE APPROACH

P

DC

SH

W

DevicesWearables, embedded and

ambient sensors continuously record data.

PatientThe patient simply wears or tolerates the sensors and optionally uses engaging and easy to use e-diaries and cognitive state-assessing games.

CloudSensor data, patient diaries and health records are stored in the cloud in a HIPAA and GDPR compliant manner.

SmartphonesSmart mobile phones are used to record location, kinematic and sound data. They are also used as processing and communication hubs.

Web ServicesData are posted online

and post-processed.

Healthcare ProfessionalTreating physicians are able to monitor 24/7 the

progression of patient symptoms, having access to objective measurements.

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HOW TECHNOLOGY CAN HELP?

Clinical problem Available/needed technologies Clinical objective

Improving diagnosisNeeded: sensors for prodromal features (e.g., constipation, REM sleep behavior, anosmia); blood sensors for biomarkers (a-synuclein, proteinomics, etc.)

Enable population screening for PD, including the earliest possible (prodromal) stages.

Monitoring response to therapy and motor complications (motor fluctuations, dyskinesia)

Available: accelerometers, gyroscopes, magnetometers, electrogoniometers, surface EMG sensors.Needed: small patches onto the skin or other sensors that improve patient adherence.

Collect ecologically valid data of motor fluctuations, falls, freezing of gait episodes. Implement sensor-based closed-loop technologies capable of delivering treatments (e.g., infusion pump)

Monitoring nonmotor symptoms and progression

Available (but requiring improvements): sweat sensors, skin conductance sensors, heart rate sensors, blood pressure sensors

Collect ecologically valid data of nonmotor symptoms and progression

Enhancing surgical treatmentAvailable (but requiring improvements): STN DBS, GPiDBS, Vim thalamus DBS

Implement closed-loop STN and GPi DBS (variable stimulation based on local field potentials)

Improving rehabilitation interventions

Available: accelerometers, gyroscopes, magnetometers, electrogoniometers, surface EMG sensors, pulse oximetry sensors, respiratory rate sensors, blood pressure sensors

Implement closed-loop cueing and feedback systems validated for home use

Source: Technology in Parkinson’s disease: Challenges and opportunities [1]

DBS, deep brain stimulation; EMG, electromyography; GPi, globus pallidus pars interna; REM, rapid eye movement; STN, subthalamic nucleus; Vim, ventrointermedial nucleus.

[1] Espay, A. J., Bonato, P., Nahab, F. B., Maetzler, W., Dean, J. M., Klucken, J., et al, the Movement Disorders Society Task Force on Technology. Technology in Parkinson’s disease: Challenges and opportunities. Movement Disorders, 2016, 31(9), 1272-82.

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TECHNOLOGY-BASED MEASURING OF EVERYDAY FUNCTION. HOW?

Studies using inertial sensors (accelerometers, gyroscopes, magnetometers) use different placements on the patient. In order to measure function as accurately as possible, more sensors are required, increasing the complexity of the proposed system. In some cases only one sensor is used but the range of symptoms quantified is limited. Gait, Freezing of Gait and Balance impairments need high frequency and more signals to be assessed.

Sensors are usually placed on the limbs and waist or chest. These sensors are either connected to a separate processing hub, or transmit the collected data to a smartphone or tablet.

Statistical analysis and machine learning (ML) algorithms are used to extract features from the signals and translate them into domain-specific, PD-related symptoms. This processing is either performed on the hub, smartphone or tablet, or on the cloud, where the data are uploaded for secure storing and provisioning.

The role of the physician is the same. However, with the new information available the treatment can be much more tailored and the advice to the patient more accurate and helpful.

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MEASURING EVERYDAY FUNCTION

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TECHNOLOGY-BASED MEASURING OF EVERYDAY FUNCTION. HOW?

Collection Processing Storing Decision

Raw data Pattern recognition and other ML Experience and Expertise

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MEASURING EVERYDAY MOTOR FUNCTION

SignalsSensory data are

collected from patient movements

ProcessingAdvanced Signal processing methods are applied in order to extract motor features related to specific PD symptoms

EvaluationMachine Learning methods are applied to

evaluate the motor features and extract clinical interpretable information

AssessmentPhysicians assess patient status based on symptom reporting

P

E

A

S

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CHALLENGES

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MEASURING EVERYDAY FUNCTION. CHALLENGES

Cost / Reimbursement

Compatibility / Interoperability

Usability / Adherence

Validation

Technology-based solutions need to be cost-effective and reimbursable.

Technology-based measures need to be validated in a clinical context.

Devices must be user-friendly, little to no interaction.

Technology-based tools need to be compatible and interoperable.

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MEASURING EVERYDAY FUNCTION. CHALLENGES

In order for the fusion of multi-source technology-based motor and nonmotor monitoring to be considered a gold standard for quantitative measurement of quality of life, rigorous validation is required. Unfortunately, clinical research endpoints do not necessarily coincide with clinical care needs.

For example, tracking the immediate response to a therapy, which could be done using a technology-based test, can be different from monitoring overall disease progression [1]. There are portions of the PD clinical examination, such as rigidity and pull testing, which cannot be adequately evaluated without an on-site assistant [2].

A modified version of the Unified Parkinson’s Disease Rating Scale (UPDRS) with rigidity and postural instability removed has been deemed reliable and valid [3].

[1] Espay, A. J., Bonato, P., Nahab, F. B., Maetzler, W., Dean, J. M., Klucken, J., et al, the Movement Disorders Society Task Force on Technology. Technology in Parkinson’s disease: Challenges and opportunities. Movement Disorders, 2016, 31(9), 1272-82.[2] Dall T.M., Storm M.V., Chakrabarti R., Drogan O., Keran C.M., Donofrio P.D., et al. Supply and demand analysis of the current and future US neurology workforce. Neurology. 2013, 81, 470-8.[3] Abdolahi A., Scoglio N., Killoran A., Dorsey E.R., Biglan K.M. Potential reliability and validity of a modified version of the unified Parkinson’s disease rating scale that could be administered remotely. Parkinsonism Relat Disord, 2013, 19, 218-21.

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MEASURING EVERYDAY FUNCTION.CHALLENGES

Studies show that passive monitoring, where little to no interaction with the technology is required,may lead to better overall compliance rates [1]. However, lack of motivation to use wearables andself-monitoring systems should be addressed by offering meaningful feedback to the users [2].

Most wearable devices used to measure everyday function are not compatible with one another oremploy proprietary technology, unavailable to the professionals using them.

Reimbursement currently is a limitation for technology-based everyday function measurementsolutions. Limited or absent reimbursement hinders their broader adoption [3].

[1] Espay, A. J., Bonato, P., Nahab, F. B., Maetzler, W., Dean, J. M., Klucken, J., et al, the Movement Disorders Society Task Force on Technology. Technology in Parkinson’s disease: Challenges and opportunities. Movement Disorders, 2016, 31(9), 1272-82.[2] Silva de Lima A.L., Hahn T., Evers L.J.W., de Vries N.M., Cohen E., Afek M., et al. Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease. PLoS ONE, 2017, 12(12), e0189161.[3] Achey M., Aldred J.L., Aljehani N., et al. The past, present, and future of telemedicine for Parkinson’s disease. Mov Disord, 2014, 29, 871-83.

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THE PDMONITOR® SOLUTION

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THE PDMONITOR® SOLUTION

22

with:

•wearable devices unobtrusively monitoring patients

24/7.

•Machine Learning and Artificial Intelligence

Algorithms to accurately monitor the most significant

PD symptoms

•mobile app and cloud infrastructure connecting all

involved actors.

offers:

•better provision of services by the physician

•better treatment for the patient

•better support to the caregiver

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THE PDMONITOR® INTENDED USEPDMonitor® is a non-invasive continuous monitoring system for use from patients with Parkinson’s disease only with the consultation of their attending physician. The system consists of:

• a set of five (5) wearable Monitoring Devices,

• a mobile application, which enables patients/caregivers to record medication, nutrition and non-motor status information as complementary information for the motor symptom assessment, and

• a physician tool, which graphically presents to the healthcare professional patient movement and patient related information.

The physician tool reports will be at the disposal and judgment of the attending healthcare professional and could allow for a better and objective assessment and understanding of the patient’s symptom condition related to the Parkinson’s Disease

The PDMonitor® system can be used at any stage of the disease after its initial diagnosis and when the patients are under medical treatment. Three actors compose the PDMonitor® user ecosystem:

(a) patients being at any stage of the disease,

(b) caregivers – formal (nurses, volunteers) or informal (relatives, family, volunteers) appointed for specific patients,

(c) healthcare professionals (medical doctors – Neurologists experts in movement disorders or Neurologists or General Practitioners trained to interpret PDMonitor® reports).

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THE PDMONITOR® ECOSYSTEM

24

The PDMonitor® monitoring devices are attached on the patient’s body and measure movement data.

PDMonitor® monitoring system

The PDMonitor® cloud backendprovides secure data storage and data services.

PDMonitor® cloud infrastructure

Provide a comprehensive web tool for the assessment of patients’ motor symptoms.

PDMonitor® physician tool

The PDMonitor® provides a mobile app for each actor (patient / physician / caregiver) of the PDMonitor® solution

PDMonitor mobile app

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THE PDMONITOR® MONITORING SYSTEM (1/2)

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PDMonitor® Monitoring Device PDMonitor® accessories PDMonitor® SmartBox

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THE PDMONITOR® MONITORING SYSTEM (2/2)

The PDMonitor® monitoring devices are mounted on a network of five (5) predefined body positions:

• Waist (1)

• Wrists (2)

• Shanks (2)

Each device collects movement data using an accelerometer, a gyroscope and a magnetometer.

The sensor data are used in order to evaluate the various PD symptoms affecting both body sides.

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PDMONITOR® PHYSICIAN TOOL (1/2)

The PDMonitor® web application tool is the main tool for the physician allowing the management of his patients and devices.

27PDMonitor physician tool snapshots

The physician has access to a dashboard for each patient with information provided by his/her

PDMonitor® monitoring system and mobile application.

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PDMONITOR® PHYSICIAN TOOL (2/2)

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PDMonitor physician tool snapshots – Activity detection (real data)

PDMonitor® physician tool provides an

interactive reporting functionality

allows physicians to explore and

compare monitoring periods and

symptom severity.

PDMonitor® physician tool provides a summary of patient motor symptom and mobility status complemented by medication and nutrition information from the mobile app.

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PDMONITOR® MOBILE APP

• Medication adherence and medication reminders

• Nutrition monitoring

• Symptom (ON/OFF/Dyskinesia) diary

• Message of the Day

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HOW PDMONITOR® WORKS

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PDMONITOR® SYMPTOM EXTRACTION

The PDMonitor® solution follows anhierarchical approach for symptomassessment.

The main idea is to first identify“regions of interest” where specificsymptoms can be evaluated with highaccuracy.

This requires an accurate activitydetection. The use of 5 sensors enablesthe accurate detection of activity.

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Activity

Gait/FOG Assessment

Walking

Tremor Detection

Dyskinesia Detection

No or Slight Tremor

No or Slight Dyskinesia

Dyskinesia Assessment

Dyskinesia

Tremor Assessment

Resting/Lying

Sleep Assessment

Sleeping

Tremor

Bradykinesia Assessment

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PDMONITOR® BODY POSITION

Accessing activity, walking and symptoms requires theidentification of the placement of the sensor.

This has initially imposed a requirement to have some kind oflabeling for sensor body position (left/right, legs/arms/waist).

A new method is developed to identify sensors automaticallygiven correct placement. The method relies on the followingobservations to identify where the sensor is placed(leg/waist/arms):

waist accelerometer energy is always less than that of the rest of sensors.

Hands are facing both upwards and downwards whereas legs are not

left and right sides (legs and arms) have different sign in the correlation ofdifferent axis of the gyroscope

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Orientation Vector

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SYMPTOMS AND PATIENT’S STATUS DETECTION

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THE PDMONITOR® ACTIVITY

Activity detection is fundamental for both patient assessment and also for symptom evaluation.

PDMonitor® detects main activity (resting/walking) and posture (lying/sitting).

This enables the better assessment of symptom impact on patient quality of life.

The use of 5 sensors enables the accurate detection of activity.

In the instructions it is clearly stated that the devices should NOT be used in intense activities such as running, swimming, bicycling etc. However, we are already in the process of recognizing specific activities and eventually extract specific measures (i.e. bicycle).

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THE PDMONITOR® GAIT

Parkinsonian gait is characterized by small shuffling steps and a general slowness of movement (hypokinesia), or even the total loss of movement (akinesia) in extreme cases.

PD Gait has reduced stride length and walking speed during free ambulation while double support duration and cadence rate are increased.

PD patients experience difficulty in starting and stopping after starting (FoG).

Kinematic parameters (stride length, swing velocity, peak velocity) related to the energy are Dopa-sensitive [1].

Temporal parameters (stride and swing duration, stride duration variability), related to rhythm, are Dopa-resistant [1].

[1] O. Blin, A.M. Ferrandez, J. Pailhous, G. Serratrice, Dopa-sensitive and Dopa-resistant gait parameters in Parkinson's disease, Journal of the Neurological Sciences, Volume 103, Issue 1, May 1991, Pages 51-54, ISSN 0022-510X.

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PDMONITOR® GAIT

36

Signal

Filtering

Activity Classification

Merge Candidate Walking Regions

Detect Steps

Identify Walking Regions

Extract Gait Features

Gait Score

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PDMONITOR® GAIT

Consecutive regions classified as walking aremerged into larger walking regions.

Larger regions provide better statisticalestimation of gait parameters.

The current implementation is based on signalfiltering and peak detection.

Three peaks are used for each step (TerminalContact - TC, maximum rotational speed andInitial Contact - IC)

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PDMONITOR® EVALUATION DATASET

Two studies PDNST001: Patients for performance and

wearability evaluation with two (2) phases:

Phase I: Hospital

Phase II: Home

PDNST002: Healthy for safety and wearability assessment.

Data from three sites (Ioannina, Venice, Dresden)

Study/Phase # Subjects Total Hours

PDNST001 Phase I 47 265

PDNST001 Phase II 22 580

PDNST002 23 238

Total 92 1083

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PDMONITOR® EVALUATION DATASET

The first day of the patient enrolmentincludes a several hour recording wherepatients are closely monitored by experts.

Every ~30 minutes a mini UPDRS is filledby an expert physician.

The symptoms extracted by PDMonitor®are compared with UPDRS scores.

Phase I: Hospital Phase II: Home

Patients are using the device unattendedeither at home or at the hospital, in caseof inpatients.

They fill a patient (symptom) diary.

The PDMonitor® extracted measures arecompared with both UDPRS of phase Iand also with patient diary.

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PDMONITOR® EVALUATION

The PDMonitor® symptom measures mayconcern the detection of a symptom oractivity presence (i.e. walking, tremor,dyskinesia) or the evaluation of a symptomseverity (i.e. gait impairment).

For symptom/activity detection theperformance is evaluated based onconfusion matrix measures such asaccuracy, sensitivity, specificity.

For severity estimation the performance isevaluated based on Pearson correlation andBland Altman analysis.

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Symptom

Detection

A B Sens. Spec.

A a b SENA SPECA

B d c SENB SPECB

Acc. Acc

Severity Estimation

Confusion matrix Correlation and Bland Altman Analysis

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PDMONITOR® GAIT

PDMonitor® is able to detect even heavily

impaired gait. With PDMonitor® Walkingdetection has 96% accuracy for PD patients.

A number of temporal parameters (stride andswing duration, stride duration variability) andKinematic parameters (stride length, swingvelocity, peak velocity) are extracted toevaluate gait.

[1] O. Blin, A.M. Ferrandez, J. Pailhous, G. Serratrice, Dopa-sensitive and Dopa-resistant gait parameters in Parkinson's disease, Journal of the Neurological Sciences, Volume 103, Issue 1, May 1991, Pages 51-54, ISSN 0022-510X,

Walking Dys. Rest Tremor Sens. Spec.

Walking 382 35 0 0 0.92 0.94

Dys. 14 750 21 11 0.94 0.92

Rest 3 10 470 17 0.94 0.92

Tremor 6 23 19 679 0.93 0.96

Acc. 0.93

Walking No Walking Sens. Spec.

Walking 382 35 0.92 0.96

No Walking 14 750 0.98 0.96Acc. 0.96

Results from train/test (66/33%) with feature selection and Naïve Bayes classifier

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PDMONITOR® BRADYKINESIA

A stepwise linear model was used to estimate the bradykinesia score for the UPDRS arm sub-score based on the features extracted in 30-min. windows.

The correlation was r=0.67 which is comparable to the 0.64 correlation of the BKS score of Kinetigraph[1] to UPDRS score for a 10 day period.

However, considering a per patient score (closer to Kinetigraph result) the correlation was 0.78.

42

[1] Griffiths, R. I., Kotschet, K., Arfon, S., Xu, Z. M., Johnson, W., Drago, J., … Horne, M. K. (2012). Automated assessment of bradykinesia and dyskinesia in Parkinson’s disease. Journal of Parkinson’s Disease, 2(1), 47–55. https://doi.org/10.3233/JPD-2012-11071

Bradykinesia is the major PD symptom and it affects the whole body but not uniformly.

The PDMonitor® solution for the assessment of bradykinesia severity is based on features extraction from the whole body for the evaluation of a patient’s movement capacity (speed, rhythm, smoothness, symmetry).

However, to assess movement capacity, movement must occur and be detected in the first place.

We discriminate hand movement during walking (arm swing) from other movements during daily activities (reading a book, drinking, eating etc.)

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PDMONITOR® DYSKINESIA

Features extracted in the 5-min windows were aggregated in 30

min. windows and compared to patient diary (dyskinesia severity).

A stepwise linear model was employed with a leave one validation

method.

The correlation between the two measures is r2>0.75.

43

PDMonitor® is able to detect dyskinesia with high

accuracy. The detection is based on a large number

of features detecting and evaluating body

“involuntary” motion. Misclassification occurs

mainly in slight dyskinesia.

No Dys. Dys. Sens. Spec.

No Dys. 290 18 0.94 0.94

Dys. 19 321 0.94 0.95

Acc. 0.94Naïve Bayes classification of dyskinesia in 5-min. window intervals with dyskinesia severity 1 cases removed. 10-fold cross validation was performed.

Posterior probability (5-min. windows) of dyskinesia wascompared to the percentage of time that according to patientdiary the patient was in ON WITH DYS.The correlation between the two measures is r2>0.9.

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PDMONITOR® TREMOR

PDMonitor® differentiates tremor in Leg and Wristtremor. Employing movement features extracted fromall 5 sensors leg tremor is accurately differentiatedfrom leg dyskinesia and walking.

Upper limb tremor is characterized by a harmonicmotion in the 3.5-7Hz band. Spectral analysis is usedto discriminate tremor from dyskinesia or otheractivity.

30-min. window leg tremor classification based on the results of the activity classifier.

No Leg Tremor Tremor Sens. Spec.

No Tremor 60 4 0.94 0.97

Tremor 2 16 0.89 0.80

Acc. 0.93

The wrist tremor severity is based on the methods developedby PD Neurotechnology R&D team [1,2,3].

PDMonitor® has r2>0.77 regarding total wrist tremor severityestimation vs expert annotation.

[1] Rigas, G., Tzallas, A. T., Tsipouras, M. G., Bougia, P., Tripoliti, E. E., Baga, D., … Konitsiotis, S. (2012). Assessment of tremor activity in the Parkinson’s disease using a set of wearable sensors. IEEE Transactions on Information Technology in Biomedicine : A Publication of the IEEE Engineering in Medicine and Biology Society, 16(3), 478–87. https://doi.org/10.1109/TITB.2011.2182616[2] Rigas, G., Tzallas, A. T., Tsalikakis, D. G., Konitsiotis, S., & Fotiadis, D. I. (2009). Real-time quantification of resting tremor in the Parkinson’s disease. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE (pp. 1306–1309). IEEE.[3] Kostikis, N., Hristu-Varsakelis, D., Arnaoutoglou, M., & Kotsavasiloglou, C. (2014). Smartphone-based evaluation of parkinsonian hand tremor: Quantitative measurements vs clinical assessment scores. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Vol. 2014, pp. 906–909). IEEE. https://doi.org/10.1109/EMBC.2014.6943738

44V1.0 APRIL 2019

PDMONITOR® FOG

PDMonitor FoG evaluation is based onthe Session Freezing Index (SFI). This indexis calculated as the number of caseswhere the FI is > 0.3 to the total numberof cases

𝑆𝐹𝐼 =#(𝐹𝐼>0.3)

#𝐹𝐼

45

No FoG FoG Sens. Spec.

No FoG 28 0 1 0.88

FoG 4 28 0.88 1

Acc. 0.93

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PDMONITOR® POSTURAL INSTABILITY

According to UPDRS postural instability is assessed based on the “push” test.However this is not feasible. An alternative measure is the swing time variabilityproposed in [1].

Classification of the gait cycle involves two main phases: the stance phase and theswing phase. Swing time variability is evaluated as a predictor of significant postureinstability per session. Based on the threshold defined in [1] the 1/3 of patients withpostural instability are detected (mainly with sever postural instability) with no falsepositives.

[1] Schlachetzki, Barth, Marxreiter, Gossler, Kohl, Reinfelder, Gassner, Aminian, Eskofier, Winkler, and Klucken, PLoS One, vol. 12, no. 10, p. e0183989, 2017.

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PDMONITOR® ON/OFF

A logistic regression model is used toestimate OFF state based on individualpatient symptoms directly monitored by thePDMonitor® device.

The Pearson correlation of the PDMonitor®OFF score with the actual OFF in patientdiary in the current dataset is 0.69(R2=0.487).

The correlation of the OFF time estimatedby PDMonitor® and the OFF timeestimated from patient diary is 0.67(R2=0.46).

47

Correlation and Bland Altman plots of PDMonitor OFF time vs Patient diary OFF time

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THE PDMONITOR® PERFORMANCE CHARACTERISTICS

48

MDS-UPDRS PD Monitor Outcome Result

- Activity/Posture Accuracy 0.93

3.4-3.6 Bradykinesia R2=0.46

3.14 Bradykinesia + Gait R2=0.79**

3.18 (Arms) Wrist Tremor Detection Accuracy 0.89, Correlation R2=0.67

3.18 (Legs) Leg Tremor Detection Accuracy 0.93

3.10 Gait Disturbances R2 =0.6

3.11 Freezing of Gait Accuracy 0.93

3.12 Postural Instability 0.59 Accuracy with 1 spec. and 0.33 sens.

Dyskinesia presence Dyskinesia Presence Accuracy 0.94

4.1 Time spent with Dyskinesia R2 = 0.91

4.2 Time spent in OFF state R2 = 0.46**

**Based on the annotations of corresponding UPDRS items

PDMonitor® performance evaluated on 71 subjects and 122 sessions with several hundred hours of data*

*21 patients/28 sessions from pilot study, 31 patients/62 sessions and 19 controls/32 sessions from PDMonitor clinical trials

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A NEW RELATIONSHIP

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PATIENT’S MANAGEMENT BECOMES, SYSTEMATIC, COMPREHENSIVE AND SCALABLE, CREATING A NEW RELATION PHYSICIAN-PATIENT-CAREGIVER

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1 2

4 3

collect build

analysesuggest

Patient + Caregiver

• Simple

• Accurate

• Fast

• Holistic (nutrition,

medication, activity) Machine Learning and Artificial Intelligence Algorithms

Doctor

• Objective

• Personalized

• Continuous

• Proactive (alerts)

• Patients prioritization

• Efficient drug administration

• Avoid Caregiver Burn out

51

www.pdneurotechnology.com

V1.0 APRIL 2019