Nada Fitrieyatul Hikmah, S.T., M.T.

Nama : Nada Fitrieyatul Hikmah, S.T., M.T.
NIP : 199001072018032001
NIDN : 0007019004
Email : nadafh[at]
Pendidikan : S1: Universitas Airlangga, Teknobiomedik (2008-2012)
  S2: ITS, Teknik Elektro – Elektronika (2013-2016)
Bidang Keahlian :
Perolehan HKI :
Profil : Curiculum Vitae | Sinta | Google Scholar | Scopus


  • 2020, Design of monitoring, control, and database systems on Hemodialysis machines based on internet of thing
  • 2020, Device-to-human communication and IoT in free fall motion experiment
  • 2020, Integration of uniformly accelerated linear motion experiment using internet networks
  • 2020, Design and implementation of remote laboratory in moment inertia experiment based on IoT
  • 2019, Diagnosis of Fatigue During Exercise with EEG Signals as Treadmill Speed Control and Post Exercise with PCG Signals, research leader
  • 2019, Finger movement learning through hand glove, research member
  • 2018, Development of Multimodal Cardiac Signal Instrumentation System As Heart Dynamics Representation, research leader
  • 2018, Development of Cardiac Signal Framework For Multimodal Analysis of Cardiodynamic Parameters, research leader
  • 2014-2016, Analisis Multimodal Sinyal Jantung (ECG, PCG dan Carotid Pulse) Untuk Klasifikasi Jantung Normal dan Abnormal

Conference Proceedings

Tahun 2016

[1] Hikmah, Nada Fitrieyatul; Arifin, Achmad; Sardjono, Tri Arief; Suprayitno, Eko Agus;, “A sequential hypothesis testing of multimodal cardiac analysis”, Asea Uninet Scientific and Plenary Meeting 2016, pp. 63-77, 2016

Focus of our research group is development of integrated cardiac analysis system. A measurement and analysis system of cardiovascular system can be realized in an integrated system that includes all cardiac vital signs [1]. We have tested a signal processing framework of multimodal cardiac signals, electrocardiogram, carotid pulse, and phonocardiogram of normal subjects [2]. This paper describes a follow up effort in analysis and classification of heart conditions. Multimodal cardiac signals were recorded from 20 normal and 3 abnormal subjects. The measurements were performed after obtaining the consent of subjects. The data were recorded using a special instrument designed by our group, and digitized with 1 kHz sampling frequency. The recordings were performed in 10 trials, with 5 second for each trial. Parameters of the cardiac signals were extracted. Sequential hypothesis testing [3] was used in classification stage to produce a diagnosis of normal and abnormal heart based on the extracted parameters. The overlapping problem was solved by selecting two thresholds, upper and lower, resulted in no decision taken while the value of data tested was in the overlapping zone. The results of normal subjects showed that 90% of the data were identified in the 3rd test and 100% of the data could be identified after the 4th test, while the abnormal subjects showed that 80% of data were identified in the 3rd test and 100% of the data could be identified after the 4th test. The classification result recommended the proposed method should be realized in clinical use.

[1] Hikmah, Nada Fitrieyatul; Arifin, Achmad; Sardjono, Tri Arief; Suprayitno, Eko Agus;, “A signal processing framework for multimodal cardiac analysis”, 2015 International Seminar on Intelligent Technology and Its Applications, ISITIA 2015 – Proceeding, pp. 125-130, IEEE, 2015

The heart is a complex organ in the cardiovascular system which its measurement and analysis system in clinical level should be realized in an integrated system including all cardiac vital signs. A previous study combined ECG and PCG analysis could detect murmur symptom. However, the heart mechanical activity could not be described. This study developed a multimodal analysis of cardiac signals consisting of ECG signals, carotid pulse, and PCG. The purpose of this study was to develop and test an appropriate signal processing framework to facilitate parameter extraction and to enhance understanding of underlying mechanisms in the cardiac physiology. Frequency and time-frequency domain analysis of cardiac signals were performed to design sophisticated digital filters. Recursive digital filters were chosen in realizing segmentation methods and the advanced signal processing techniques were performed in parameter extraction. Results show the proposed method was able to detect QRS complex, P and T waves in ECG signal with 88% sensitivity and also percussion wave with 85.62% sensitivity. Sistolic (S1) and diastolic (S2) heart sound also could be separated. Classification of normal and the disease type of heart based on the cardiac parameters resulted by the presented signal processing framework would be next research topic.