OBJECTIVE: Cardiac diseases are a leading cause of global mortality. Electrocardiograms (ECGs) are essential for detecting abnormal cardiac rhythms. Smartwatches can record ECGs, similar to lead I ECGs recorded by a patient vitals monitor in a hospital, potentially helping clinicians in early diagnosis and improved management of cardiovascular diseases. While AI models have classified arrhythmias with human-level accuracy, their potential for broad screening remains underutilized. METHODS: We propose a deep learning based framework for diagnosing various cardiac arrhythmias using 10-second lead I ECG recordings, demonstrating lead I's utility in remote monitoring. Robustness was tested by introducing noise to simulate real-world conditions. Additionally, a novel data similarity assessment metric was developed to enhance transfer learning and external dataset validation. RESULTS: Using over 60,000 ECGs from the PhysioNet Challenge 2021, the trained model classified clean lead I ECGs in one dataset with a test-fold area under receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.915, 0.867 and 0.858 respectively. For signals with 0 dB signal-to-noise ratio from the same dataset, the respective performance metrics dropped slightly to 0.899, 0.862 and 0.818. External validation across three separate datasets showed a minimum AUC of 0.807. The data similarity metric outperformed an existing method in improving classification, particularly with limited target dataset samples, i.e. 50. CONCLUSION: The proposed Cardiac Arrhythmia Risk Evaluation from Lead-I ECG (CARE-I) framework enables accurate arrhythmia detection across diverse populations in real-world noisy environments, thus enhancing model generalisation and early diagnosis.

Fault diagnosis is turning out to be an intense challenge due to the increasing complexity of the emerging cellular networks. The root-cause analysis of coverage-related network anomalies is traditionally carried out by human experts. However, due to the vast complexity and the increasing cell density of the emerging cellular networks, it is neither practical nor financially viable. To address this, many studies are proposing artificial intelligence (AI)-based solutions using minimization of drive test (MDT) reports. Nowadays, the focus of existing studies is either on diagnosing faults in a single base station (BS) only or diagnosing a single fault in multiple BS scenarios. Moreover, they do not take into account training data sparsity (varying user equipment (UE) densities). Inspired by the emergence of convolutional neural networks (CNN), in this paper, we propose a framework combining CNN and image inpainting techniques for root-cause analysis of multiple faults in multiple base stations in the network that is robust to the sparse MDT reports, BS locations and types of faults. The results demonstrate that the proposed solution outperforms several other machine learning models on highly sparse UE density training data, which makes it a robust and scalable solution for self-healing in a real cellular network.

In the advent of ultra-dense networks with unprecedented complex and heterogeneous infrastructure, the role of automation in network optimization becomes vital for sustaining the target performance. In this work, we address the challenge of identifying and classifying sub-par performing nodes in near-real time through a machine-learning inspection of streaming performance indicators from multiple probe points. We present a novel K-means-based solution for classifying node performance over a sliding time segment and further categorizing the type of failure. The K-means solution first identifies the performance instances of interest. These are then inspected in a second clustering round for automated performance labeling. Next, the labeled data-set is employed to train a Support Vector Machine based classifier that is continuously classifying incoming performance instances from the network. The method is tested using a real network data set comprising call detail records. The results advocate the potential of our method for effectively and accurately identifying and classifying performance degradation in any node in the network.

Understanding of mobile internet traffic patterns and capacity to estimate future traffic, particularly at high spatiotemporal granularity, is crucial for proactive decision making in emerging and future cognizant cellular networks enabled with self-organizing features. It becomes even more important in the world of `Internet of Things' with machines communicating locally. In this paper, internet activity data from a mobile network operator Call Detail Records (CDRs) is analysed at high granularity to study the spatiotemporal variance and traffic patterns. To estimate future traffic at high granularity, a Support Vector Regression (SVR) based traffic model is trained and evaluated for the prediction of maximum, minimum and average internet traffic in the next hour based on the actual traffic in the last hour. Performance of the model is compared with that of the State-of-the-Art (SOTA) deep learning models recently proposed in the literature for the same data, same granularity, and same predicates. It is concluded that this SVR model outperforms the SOTA deep and non-deep learning methods used in the literature.

Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this, we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, the proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better QoS.