BIOGRAPHY
Dr. Zhiping Lin received the B. Eng degree from South China Institute of Technology, China in 1982, and the Ph.D. degree from the University of Cambridge, England in 1987. Subsequently, he worked as a postdoctoral researcher at the University of Calgary, Canada. He was an associate professor at Shantou University, China from 1988 to 1993, and a senior engineer at DSO National Laboratories, Singapore from 1993 to 1999. Since Feb. 1999, he has been an associate professor at the School of Electrical and Electronic Engineering (EEE), Nanyang Technological University (NTU), Singapore.
Dr. Lin was the Editor-in-Chief of Multidimensional Systems and Signal Processing for 2011-2015, and has been in its editorial board since 1993. He was an Associate Editor of IEEE Transactions on Circuits and Systems – II for 2010-2011, and a Subject Editor of the Journal of the Franklin Institute for 2015 – 2019. Dr Lin is a member of the Digital Signal Processing Technical Committee (TC) and the Biomedical Circuits and Systems TC of the IEEE Circuits and Systems Society. He has served in organizing/technical committees of various international conferences, including General Chair of ICICS 2013.
Dr. Lin is the co-author of the 2007 Young Author Best Paper Award from the IEEE Signal Processing Society, and received several Best Paper Awards in international conferences. He was Distinguished Lecturer of the IEEE Circuits and Systems Society (CAS) for 2007-2008, and served as Chair of IEEE CAS Singapore Chapter for 2007-2008, 2019.
PATENTS
US-2019-0125263-A1 : System And Method For Health Condition Monitoring (2021)
Abstract: A system for health condition monitoring includes a wearable device, a portable device and a server. The portable device is capable of communicating between the wearable device and the server. The system further includes a non-contact ECG acquisition module for capturing ECG signals from a user wearing the wearable device, a non-contact audio acquisition module for capturing a respiratory sound signal and a heart sound signal from the user wearing the wearable device, a first signal processing and analysis module for receiving and processing the ECG signals, the respiratory sound signal and the heart sound signal to perform QRS detection, HR calculation and ECG derived RR determination, and a second signal processing and analysis module for receiving and processing the ECG signals, the respiratory sound signal and the heart sound signal to perform heart sound localization, heart sound cancellation, respiratory sound restoration, and sound based RR determination.
US 2014/0257063 A1: Method Of Predicting Acute Cardiopulmonary Events And Survivability Of A Patient (2015)
Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.
US 2014/0187988 A1: Method Of Predicting Acute Cardiopulmonary Events And Survivability Of A Patient (2015)
Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.
