Empirical mode decomposition of heart rate variability. A methodological study

Schiecke, Karin; Piper, Diana; Buerger, Stefanie; Leistritz, Lutz; Feucht, Martha; Witte, Herbert

doi:10.1109/esgco.2014.6847541

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…EMD decomposes a signal x( n ) into the s um 0 fa num ber of intrinsic mode functions (lMFs): x(n) = L�= l aq(n) + r(n) , wh ere a/n) is th e IMF, r(n) is th e residual. It ha s been shown to be efficient in some areas [8][9].…”

Section: Introductionmentioning

confidence: 99%

“…echniques to solve(9 ) and g et t he s parse coefficient z .In N GPSR a Igorithm, a ffected by t he no nnegative constraint, x is e xpressed:x = U, U ') = {(V'F(Z (k»)) ;oZ; ') >Oor(V'F( z(k»)) , <0 0, el seThen replace a o by mid(amin,a O ,am,,) .Choose a (k) to bet he first number in t he s equence (Z(k») _ ttY' F(z('») , (z(') -(zI')a (k)V' F(zIk»)) + ),t hen set Z(k +l ) = (Z(k ) -aV' F(Z(k ) )) + .4Go t 0 step 2 u ntil termination, output t he sparse reconstruction coefficients z .…”

mentioning

confidence: 99%

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Radar target recognition based on dictionary of time-frequency feature and nonnegative sparse decomposition

Kong

Wang

2015

2015 12th International Bhurban Conference on Applied Sciences and Technology (IBCAST)

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A new approach for radar HRRP target recognition is presented in this paper which combines the empirical mode decomposition (EMD) method with the nonnegative gradient projection for sparse reconstruction (NGPSR) method. EMD is used to extract the feature vector for dictionary learning and NGPSR is used to reconstruct the HRRP signal. The radar HRRPs are classified according to the sum of the sparse representation coefficients. The experimental results based on simulated radar HRRP targets recognition show that the proposed method can achieve a higher correct recognition rate compared with classical classification methods.I.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Radar target recognition based on dictionary of time-frequency feature and nonnegative sparse decomposition

Kong

Wang

2015

2015 12th International Bhurban Conference on Applied Sciences and Technology (IBCAST)

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“…Schiecke et al [81] investigate merits and limitations of EMD and one of its variants to study heart rate variability.…”

Section: Previous Applications and Modifications Of The Hhtmentioning

confidence: 99%

Gait Features and their Relationships to Physiological Attributes of Multiple Sclerosis

Qureshi

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To Riaz and Saira AcknowledgementsI would like to sincerely thank my advisor, Dr. Maite Brandt-Pearce, for her relentless support, understanding, and patience. It was her persistent encouragement which pushed me to be my best and guided me to the right direction whenever I was directionless. She inspired me not only as a role model but also as a friend. Her camaraderie, exemplary work ethic, determination, and emphasis on participating in group meetings, seminars, and social activities fostered my multi-dimensional technical skills and shaped me as an independent researcher. At the same time, it instilled team spirit, mutual respect, empathy, flexibility, and numerous other personal and interpersonal skills in me, which I will cherish for the rest of my life. Assessments of MS-associated cognitive and motor disability, the disease course and its progression, and decision-making regarding disease-modifying treatments and symptoms management, are based on clinical observations, comprised of outcomes of physical examinations and medical imaging, and patient-rated questionnaires. Being reliant on physicians' judgment in interpreting imaging and clinical outcomes, affected by the differences among individuals regarding the notion of disability or improvements, time-consuming, imprecise, having limited sensitivity to subtle changes in gait, and low variance in ratings are some of the drawbacks of current subjective evaluations. Moreover, patient-reported outcomes (PROs) are subject to response shifts due to changes within individuals over time regarding health standards, and could lead to confusing findings and discrepancies between expected and observed indicators, negatively impacting disease prognosis. We intend to augment existing information, on-going research, and currently-used speed and distance-based clinical assessments, for a neurologic condition, with new, objective, and clinically meaningful anchors. Although our goals are motivated for a target application (finding physiologically meaningful gait features for assessing functional quality in MS using inertial gait data), our test measures could be adopted for gait assessments and monitoring in other neurological disorders, balance, stability, and fall risk prediction, and general health and wellness applications.We derive inertial features using angular rate gait data collected using a body sensor node for improved gait assessment with three goals -(i) using variations in gait features over time, i.e., gait dynamics, to remove the inter-subject variability and guide personalized assessments in neurology-affected locomotion, aging, or chronic diseases, (ii) finding computationally efficient and robust gait features that neither require identification of exact gait cycles nor need a large dataset to capture gait deterioration and make physical sense, (iii) translating pathology-induced fluctuations into frequency-domain features to identify the impact of MS on important gait phases.We include gait variables for comparable controls in the study to determine ...

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Demonstrating the real-world significance of the mid-swing to heel strike part of the gait cycle using spectral features

Qureshi

Engelhard

Brandt-Pearce

et al. 2017

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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Empirical mode decomposition of heart rate variability. A methodological study

Cited by 5 publications

References 7 publications

Radar target recognition based on dictionary of time-frequency feature and nonnegative sparse decomposition

Radar target recognition based on dictionary of time-frequency feature and nonnegative sparse decomposition

Gait Features and their Relationships to Physiological Attributes of Multiple Sclerosis

Demonstrating the real-world significance of the mid-swing to heel strike part of the gait cycle using spectral features

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