Bishwadeep Das

1. Active Semi-Supervised Learning for Diffusions on Graphs
   B. Das; E. Isufi; G. Leus;
   In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
   pp. 9075-9079, 2020. DOI: 10.1109/ICASSP40776.2020.9054300
   document

2. Activity Dependent Multichannel ADC Architecture using Level Crossing Quantisation for Atrial Electrogram Recording
   Aurojyoti Das; Samprajani Rout; Alessandro Urso; Wouter A. Serdijn;
   In Proc. IEEE Biomedical Circuits and Systems Conference (BioCAS 2019),
   IEEE, October 17-19 2019.
   document

3. Reference-Free Calibration in Sensor Networks
   Raj Thilak Rajan; Rob-van Schaijk; Anup Das; Jac Romme; Frank Pasveer;
   IEEE Sensor letters,
   Volume 2, Issue 3, pp. 1-4, Sept. 2018. DOI: 10.1109/LSENS.2018.2866627
   document

4. Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout
   Anup Das; Paruthi Pradhapan; Willemijn Groenendaal; Prathyusha Adiraju; Raj Thilak Rajan; Francky Catthoor; Siebren Schaafsma; Jeffrey L. Krichmar; Nikil Dutt; Chris {Van Hoof};
   Neural Networks,
   Volume 99, pp. 134-147, 2018. DOI: https://doi.org/10.1016/j.neunet.2017.12.015
   
   Keywords: ...

   Electrocardiogram (ECG), Spiking neural networks, Liquid state machine, Spike timing dependent plasticity (STDP), Homeostatic plasticity, Fuzzy c-Means clustering.
   
   
   Abstract: ...

   Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine learning technique to estimate heart-rate from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery-life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects is considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.
   
   document

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