Long short-term memory (LSTM) has emerged as a definitive network for analyzing and inferring time series data. LSTM has the capability to extract spectral features and a mixture of temporal features. Due to this benefit, a similar feature extraction method is explored for the spiking counterparts targeting time-series data. Though LSTMs perform well in their spiking form, they tend to be compute and power intensive. Addressing this issue, this work proposes Multi-Compartment Leaky (MCLeaky) neuron as a viable alternative for efficient processing of time series data. The MCLeaky neuron, derived from the Leaky Integrate and Fire (LIF) neuron model, contains multiple memristive synapses interlinked to form a memory component, which emulates the human brain's Hippocampus region. The proposed MCLeaky neuron based Spiking Neural Network model and its quantized variant were benchmarked against state-of-the-art (SOTA) Spiking LSTMs to perform human stress detection, by comparing compute requirements, latency and real-world performances on unseen data with models derived through Neural Architecture Search (NAS). Results show that networks with MCLeaky activation neuron managed a superior accuracy of 98.8% to detect stress based on Electrodermal Activity (EDA) signals, better than any other investigated models, while using 20% less parameters on average. MCLeaky neuron was also tested for various signals including EDA Wrist and Chest, Temperature, ECG, and combinations of them. Quantized MCLeaky model was also derived and validated to forecast their performance on hardware architectures, which resulted in 91.84% accuracy. The neurons were evaluated for multiple modalities of data towards stress detection, which resulted in energy savings of 25.12x to 39.20x and EDP gains of 52.37x to 81.9x over ANNs, while offering a best accuracy of 98.8% when compared with the rest of the SOTA implementations.